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Zhou X, Daniel BL, Hargreaves BA, Lee PK. Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction. Magn Reson Med 2024; 92:2343-2357. [PMID: 39051729 DOI: 10.1002/mrm.30221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/03/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding. METHODS Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast. RESULTS The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI. CONCLUSION Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.
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Affiliation(s)
- Xuetong Zhou
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Bruce L Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Philip K Lee
- Department of Radiology, Stanford University, Stanford, California, USA
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Thompson RB, Sherrington R, Beaulieu C, Kirkham A, Paterson DI, Seres P, Grenier J. Reference Values for Water-Specific T1 of the Liver at 3 T: T2*-Compensation and the Confounding Effects of Fat. J Magn Reson Imaging 2024; 60:2063-2075. [PMID: 38305588 DOI: 10.1002/jmri.29262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND T1 mapping of the liver is confounded by the presence of fat. Multiparametric T1 mapping combines fat-water separation with T1-weighting to enable imaging of water-specific T1 (T1Water), proton density fat fraction (PDFF), and T2* values. However, normative T1Water values in the liver and its dependence on age/sex is unknown. PURPOSE Determine normative values for T1Water in the liver with comparison to MOLLI and evaluate a T2*-compensation approach to reduce T1 variability. STUDY TYPE Prospective observational; phantoms. POPULATIONS One hundred twenty-four controls (56 male, 18-75 years), 50 patients at-risk for liver disease (18 male, 30-76 years). FIELD STRENGTH/SEQUENCE 2.89 T; Saturation-recovery chemical-shift encoded T1 Mapping (SR-CSE); MOLLI. ASSESSMENT SR-CSE provided T1Water measurements, PDFF and T2* values in the liver across three slices in 6 seconds. These were compared with MOLLI T1 values. A new T2*-compensation approach to reduce T1 variability was evaluated test/re-test reproducibility. STATISTICAL TESTS Linear regression, ANCOVA, t-test, Bland and Altman, intraclass correlation coefficient (ICC). P < 0.05 was considered statistically significant. RESULTS Liver T1 values were significantly higher in healthy females (F) than males (M) for both SR-CSE (F-973 ± 78 msec, M-930 ± 72 msec) and MOLLI (F-802 ± 55 msec, M-759 ± 69 msec). T1 values were negatively correlated with age, with similar sex- and age-dependencies observed in T2*. The T2*-compensation model reduced the variability of T1 values by half and removed sex- and age-differences (SR-CSE: F-946 ± 36 msec, M-941 ± 43 msec; MOLLI: F-775 ± 35 msec, M-770 ± 35 msec). At-risk participants had elevated PDFF and T1 values, which became more distinct from the healthy cohort after T2*-compensation. MOLLI systematically underestimated liver T1 values by ~170 msec with an additional positive T1-bias from fat content (~11 msec/1% in PDFF). Reproducibility ICC values were ≥0.96 for all parameters. DATA CONCLUSION Liver T1Water values were lower in males and decreased with age, as observed for SR-CSE and MOLLI acquisitions. MOLLI underestimated liver T1 with an additional large positive fat-modulated T1 bias. T2*-compensation removed sex- and age-dependence in liver T1, reduced the range of healthy values and increased T1 group differences between healthy and at-risk groups. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Richard B Thompson
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Rachel Sherrington
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Christian Beaulieu
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Amy Kirkham
- Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, Ontario, Canada
| | - David I Paterson
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Peter Seres
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Justin Grenier
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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Muslu Y, Tamada D, Roberts NT, Cashen TA, Mandava S, Kecskemeti SR, Hernando D, Reeder SB. Free-breathing, fat-corrected T 1 mapping of the liver with stack-of-stars MRI, and joint estimation of T 1, PDFF, R 2 * , and B 1 + . Magn Reson Med 2024; 92:1913-1932. [PMID: 38923009 DOI: 10.1002/mrm.30182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 05/03/2024] [Accepted: 05/16/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat andB 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition. THEORY AND METHODS Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T. RESULTS The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps. CONCLUSION The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, andB 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.
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Affiliation(s)
- Yavuz Muslu
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daiki Tamada
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | | | | | | | - Diego Hernando
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Ohno Y, Ueda T, Nomura M, Sano Y, Yamamoto K, Shinohara M, Ikedo M, Yui M, Iwase A, Nagata H, Yoshikawa T, Takenaka D, Tomita A, Fujita N, Ozawa Y. Proton Density Fat Fraction Quantification (PD-FFQ): Capability for hematopoietic ability assessment and aplastic anemaia diagnosis of adults. Magn Reson Imaging 2024; 114:110240. [PMID: 39353515 DOI: 10.1016/j.mri.2024.110240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Revised: 09/06/2024] [Accepted: 09/27/2024] [Indexed: 10/04/2024]
Abstract
PURPOSE The purpose of this study was to determine the capability of proton density with fat fraction (PD-FFQ) imaging to help assess hematopoietic ability and diagnose aplastic anemia in adults. METHODS Between January 2021 and March 2023, patients diagnosed with aplastic anemia (AA: n = 14) or myelodysplastic syndrome (MDS: n = 14) were examined by whole-body PD-FFQ imaging, and 14 of 126 age and gender matched patients who had undergone the same PD-FFQ imaging were selected as control group. All proton density fat fraction (PDFF) index evaluations were then performed by using regions of interest (ROIs). Pearson's correlation was used to determine the relationship between blood test results and each quantitative index, and ROC-based positive test and discrimination analyses to compare capability to differentiate the AA from the non-AA group. Finally, sensitivity, specificity and accuracy of all quantitative indexes were compared by means of McNemar's test. RESULTS Mean PDFF, standard deviation (SD) and percentage of coefficient of variation (%CV) for vertebrae showed significant correlation with blood test results (-0.52 ≤ r ≤ -0.34, p < 0.05). Specificity (SP) and accuracy (AC) of %CV of PDFF in vertebrae were significantly higher than those of mean PDFF in vertebrae and the posterior superior iliac spine (SP: p = 0.0002, AC: p = 0.0001) and SD of PDFF in vertebrae (SP: p = 0.008, AC: p = 0.008). Moreover, AC of SD of PDFF in vertebrae was significantly higher than that of mean PDFF in vertebrae and the posterior superior iliac spine (p = 0.03). CONCLUSION Whole-body PD-FFQ imaging is useful for hematopoietic ability assessment and diagnosis of aplastic anemia in adults.
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Affiliation(s)
- Yoshiharu Ohno
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
| | - Takahiro Ueda
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Masahiko Nomura
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yuichiro Sano
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Kaori Yamamoto
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | | | - Masato Ikedo
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Masao Yui
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Akiyoshi Iwase
- Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan
| | - Hiroyuki Nagata
- Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takeshi Yoshikawa
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Daisuke Takenaka
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan; Department of Diagnostic Radiology, Hyogo Cancer Center, Akashi, Hyogo, Japan
| | - Akihiro Tomita
- Department of Hematology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Nobuyuki Fujita
- Department of Orthopaedic Surgery, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Yoshiyuki Ozawa
- Department of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
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Roach KE, Bird AL, Pedoia V, Majumdar S, Souza RB. Automated evaluation of hip abductor muscle quality and size in hip osteoarthritis: Localized muscle regions are strongly associated with overall muscle quality. Magn Reson Imaging 2024; 111:237-245. [PMID: 38636675 DOI: 10.1016/j.mri.2024.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
Limited information exists regarding abductor muscle quality variation across its length and which locations are most representative of overall muscle quality. This is exacerbated by time-intensive processes for manual muscle segmentation, which limits feasibility of large cohort analyses. The purpose of this study was to develop an automated and localized analysis pipeline that accurately estimates hip abductor muscle quality and size in individuals with mild-to-moderate hip osteoarthritis (OA) and identifies regions of each muscle which provide best estimates of overall muscle quality. Forty-four participants (age 52.7 ± 16.1 years, BMI 23.7 ± 3.4 kg/m2, 14 males) with and without mild-to-moderate radiographic hip OA were recruited for this study. Unilateral hip magnetic resonance (MR) images were acquired on a 3.0 T MR scanner and included axial T1-weighted fast spin echo and 3D axial Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL-IQ) spoiled gradient-recalled echo (SPGR) with multi-peak fat spectrum modeling and single T2* correction. A three dimensional (3D) V-Net convolutional neural network was trained to automatically segment the gluteus medius (GMED), gluteus minimus (GMIN), and tensor fascia lata (TFL) on axial IDEAL-IQ. Agreement between manual and automatic segmentation and associations between axial fat fraction (FF) estimated from IDEAL-IQ and overall muscle FF were evaluated. Dice scores for automatic segmentation were 0.94, 0.87, and 0.91 for GMED, GMIN, and TFL, respectively. GMED, GMIN, and TFL volumetric and FF measures were strongly correlated (r: 0.92-0.99) between automatic and manual segmentations, where all values fell within the 95% limits of agreement of [-9.79 cm3, 17.43 cm3] and [-1.99%, 2.89%], respectively. Axial FF was significantly associated with overall FF with the strongest correlations at 50%, 50%, and 65% the length of the GMED, GMIN, and TFL muscles, respectively (r: 0.93-0.97). An automated and localized analysis can provide efficient and accurate estimates of hip abductor muscle quality and size across muscle length. Specific regions of the muscle may be used to estimate overall muscle quality in an abbreviated evaluation of muscle quality.
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Affiliation(s)
- Koren E Roach
- Department of Radiology and Biomedical Imaging, University of California - San Francisco, 185 Berry Street, Suite 190, Lobby 6, San Francisco, CA 94107, USA; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Foothills Medical Centre, 1403-29th Street NW, Calgary, AB T2N 2T9, Canada.
| | - Alyssa L Bird
- Department of Physical Therapy and Rehabilitation Science, University of California - San Francisco, 1500 Owens Street, Suite 400, San Francisco, CA 94158, USA.
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California - San Francisco, 185 Berry Street, Suite 190, Lobby 6, San Francisco, CA 94107, USA.
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California - San Francisco, 185 Berry Street, Suite 190, Lobby 6, San Francisco, CA 94107, USA.
| | - Richard B Souza
- Department of Radiology and Biomedical Imaging, University of California - San Francisco, 185 Berry Street, Suite 190, Lobby 6, San Francisco, CA 94107, USA; Department of Physical Therapy and Rehabilitation Science, University of California - San Francisco, 1500 Owens Street, Suite 400, San Francisco, CA 94158, USA.
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Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Han F, Gao C, Shih SF, Dai Q, Curiel O, Tsao TC, Wu HH, Deshpande V. Accelerated free-breathing liver fat and R 2 * quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation. Magn Reson Med 2024; 92:1149-1161. [PMID: 38650444 DOI: 10.1002/mrm.30117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 02/25/2024] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k-space undersampling. METHODS A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. RESULTS Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. CONCLUSION This proposed method may allow accelerated free-breathing liver PDFF andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.
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Affiliation(s)
- Xiaodong Zhong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Marcel D Nickel
- MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany
| | | | - Brian M Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Cary, North Carolina, USA
| | - Fei Han
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Chang Gao
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Qing Dai
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Omar Curiel
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Tsu-Chin Tsao
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Vibhas Deshpande
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Austin, Texas, USA
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Suzuki M, Hayashi T, Nashiki K, Kawata H, Nagata S, Abe T. Influence of Gd-EOB-DTPA on proton-density fat fraction in the liver using chemical shift-encoded magnetic resonance imaging at 3-T. Radiol Phys Technol 2024; 17:637-644. [PMID: 38730134 DOI: 10.1007/s12194-024-00811-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/12/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) and its advanced stage, non-alcoholic steatohepatitis (NASH), have become increasingly prevalent owing to the rise in metabolic syndromes. Accurate assessment of hepatic fat deposition and inflammation is crucial for diagnosing and managing NAFLD/NASH. We investigated the influence of Gd-EOB-DTPA, (EOB) on proton-density fat fraction (PDFF) measurements using chemical shift-encoded magnetic resonance imaging (CSE-MRI) at 3-T. In total, 431 patients who underwent EOB contrast-enhanced MRI were included. PDFF measurements were obtained from pre- and post-contrast CSE-MRI. Linear regression and Bland-Altman analyses were performed to assess the correlation and agreement between pre- and post-EOB PDFF measurements. Relative enhancement (RE) of the liver was calculated as an EOB uptake index. There was a significant decrease in PDFF following EOB administration compared with the pre-contrast values (P < 0.0001), which was observed across all PDFF ranges (< 10% and ≥ 10%). Linear regression analysis revealed high correlation between pre- and post-EOB PDFF measurements. Bland-Altman analysis indicated a small bias between pre- and post-EOB PDFF values. Subgroup analysis based on RE showed a significant difference in ΔPDFF between patients with high RE (> 120%) and those with lower RE levels. EOB administration resulted in a slight decrease in PDFF measurements obtained using CSE-MRI at 3-T. We were able to generalize and clarify that the PDFF of the liver on 3D CSE-MRI at 3-T was slightly decreased after EOB administration as we used a larger group of patients compared to previous studies.
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Affiliation(s)
- Makoto Suzuki
- Department of Radiology, Kurume University Hospital, Asahimachi 67, Kurume, Japan.
| | - Tatsuya Hayashi
- Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, 2-11-1, Kaga, Itabashi-Ku, Tokyo, Japan
| | - Kazutaka Nashiki
- Department of Radiology, Kurume University Hospital, Asahimachi 67, Kurume, Japan
| | - Hidemichi Kawata
- Department of Radiology, Kurume University Hospital, Asahimachi 67, Kurume, Japan
| | - Shuji Nagata
- Department of Radiology, Kurume University School of Medicine, Asahimachi 67, Kurume, Japan
| | - Toshi Abe
- Department of Radiology, Kurume University School of Medicine, Asahimachi 67, Kurume, Japan
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Stelter J, Weiss K, Wu M, Raspe J, Braun P, Zöllner C, Karampinos DC. Dixon-based B 0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging. Magn Reson Med 2024. [PMID: 39155406 DOI: 10.1002/mrm.30261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 07/30/2024] [Accepted: 07/30/2024] [Indexed: 08/20/2024]
Abstract
PURPOSE To develop a Dixon-basedB 0 $$ {\mathrm{B}}_0 $$ self-navigation approach to estimate and correct temporalB 0 $$ {\mathrm{B}}_0 $$ variations in radial stack-of-stars gradient echo imaging for quantitative body MRI. METHODS The proposed method estimates temporalB 0 $$ {\mathrm{B}}_0 $$ variations using aB 0 $$ {\mathrm{B}}_0 $$ self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. TheB 0 $$ {\mathrm{B}}_0 $$ self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodicB 0 $$ {\mathrm{B}}_0 $$ variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporalB 0 $$ {\mathrm{B}}_0 $$ variations on the field-map, proton density fat fraction (PDFF) andT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ map, and to validate the proposed method. RESULTS TemporalB 0 $$ {\mathrm{B}}_0 $$ variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation ofT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ , while PDFF mapping was less affected. TheB 0 $$ {\mathrm{B}}_0 $$ self-navigator captured slowly varying temporalB 0 $$ {\mathrm{B}}_0 $$ drifts and temporal variations caused by respiratory motion. While the 1D correction effectively correctedB 0 $$ {\mathrm{B}}_0 $$ drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporalB 0 $$ {\mathrm{B}}_0 $$ variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map andT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ and reduced image artifacts. CONCLUSION TemporalB 0 $$ {\mathrm{B}}_0 $$ variations particularly affectT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct forB 0 $$ {\mathrm{B}}_0 $$ drift and physiological motion-inducedB 0 $$ {\mathrm{B}}_0 $$ variations, especially in the presence of fat.
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Affiliation(s)
- Jonathan Stelter
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | | | - Mingming Wu
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Radiology, LMU University Hospital, Munich, Germany
| | - Johannes Raspe
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Philipp Braun
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Christoph Zöllner
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, Germany
- Munich Data Science Institute, Technical University of Munich, Garching, Germany
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Jerban S, Shaterian Mohammadi H, Athertya JS, Afsahi AM, Shojaeiadib N, Moazamian D, Ward SR, Woods G, Chung CB, Du J, Chang EY. Significant age-related differences between lower leg muscles of older and younger female subjects detected by ultrashort echo time magnetization transfer modeling. NMR IN BIOMEDICINE 2024:e5237. [PMID: 39155273 DOI: 10.1002/nbm.5237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 07/23/2024] [Accepted: 07/25/2024] [Indexed: 08/20/2024]
Abstract
Magnetization transfer (MT) magnetic resonance imaging (MRI) can be used to estimate the fraction of water and macromolecular proton pools in tissues. MT modeling paired with ultrashort echo time acquisition (UTE-MT modeling) has been proposed to improve the evaluation of the myotendinous junction and fibrosis in muscle tissues, which the latter increases with aging. This study aimed to determine if the UTE-MT modeling technique is sensitive to age-related changes in the skeletal muscles of the lower leg. Institutional review board approval was obtained, and all recruited subjects provided written informed consent. The legs of 31 healthy younger (28.1 ± 6.1 years old, BMI = 22.3 ± 3.5) and 20 older (74.7 ± 5.5 years old, BMI = 26.7 ± 5.9) female subjects were imaged using UTE sequences on a 3 T MRI scanner. MT ratio (MTR), macromolecular fraction (MMF), macromolecular T2 (T2-MM), and water T2 (T2-W) were calculated using UTE-MT modeling for the anterior tibialis (ATM), posterior tibialis (PTM), soleus (SM), and combined lateral muscles. Results were compared between groups using the Wilcoxon rank sum test. Three independent observers selected regions of interest (ROIs) and processed UTE-MRI images separately, and the intraclass correlation coefficient (ICC) was calculated for a reproducibility study. Significantly lower mean MTR and MMF values were present in the older compared with the younger group in all studied lower leg muscles. T2-MM showed significantly lower values in the older group only for PTM and SM muscles. In contrast, T2-W showed significantly higher values in the older group. The age-related differences were more pronounced for MMF (-17 to -19%) and T2-W (+20 to 47%) measurements in all muscle groups compared with other investigated MR measures. ICCs were higher than 0.93, indicating excellent consistency between the ROI selection and MRI measurements of independent readers. As demonstrated by significant differences between younger and older groups, this research emphasizes the potential of UTE-MT MRI techniques in evaluating age-related skeletal muscle changes.
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Affiliation(s)
- Saeed Jerban
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Jiyo S Athertya
- Department of Radiology, University of California, San Diego, CA, USA
| | | | | | - Dina Moazamian
- Department of Radiology, University of California, San Diego, CA, USA
| | - Samuel R Ward
- Department of Orthopaedic Surgery, University of California San Diego, San Diego, CA, USA
| | - Gina Woods
- Department of Medicine, University of California, San Diego, CA, USA
| | - Christine B Chung
- Department of Radiology, University of California, San Diego, CA, USA
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Jiang Du
- Department of Radiology, University of California, San Diego, CA, USA
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Eric Y Chang
- Department of Radiology, University of California, San Diego, CA, USA
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
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10
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Yuan K, Liu Q, Luo P, Wang C, Zhou Y, Qi F, Zhang Q, Huang X, Qiu B. Association of proton-density fat fraction with osteoporosis: a systematic review and meta-analysis. Osteoporos Int 2024:10.1007/s00198-024-07220-3. [PMID: 39129009 DOI: 10.1007/s00198-024-07220-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024]
Abstract
This study aimed to evaluate the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, assessing its effectiveness as a biomarker for osteoporosis. A systematic review was conducted by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist. Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a mean difference of 11.04 (95% CI: 9.17 to 12.92, Z=11.52, P < 0.00001). Measuring PDFF via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care. OBJECTIVE This study aims to assess the correlation between measuring proton-density fat fraction (PDFF) in bone marrow using multi-echo chemical shift-encoded MRI and osteoporosis, evaluating its effectiveness as a biomarker for osteoporosis. MATERIALS AND METHODS This systematic review was carried out by two independent researchers using Cochrane, PubMed, EMBASE, and Web of Science databases up to December 2023. Quality assessments were evaluated using the Cochrane risk of bias tool and the Agency for Healthcare Research and Quality (AHRQ) checklist. RESULTS Fourteen studies involving 1495 patients were analyzed. The meta-analysis revealed a significant difference in PDFF values between the osteoporosis/osteopenia group and the normal control group, with a (MD = 11.04, 95% CI: 9.17 to 12.92, Z = 11.52, P < 0.00001). Subgroup analyses indicated that diagnostic methods, gender, and echo length did not significantly impact the PDFF-osteoporosis association. CONCLUSION PDFF measurement via MRI shows potential as an osteoporosis biomarker and may serve as a risk factor for osteoporosis. This insight opens new avenues for future diagnostic and therapeutic strategies, potentially improving osteoporosis management and patient care.
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Affiliation(s)
- Kecheng Yuan
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Qingyun Liu
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Penghui Luo
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Changliang Wang
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Yufu Zhou
- Anhui Fuqing Medical Equipment Co., Ltd., Hefei, China
| | - Fulang Qi
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Qing Zhang
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
| | - Xiaoyan Huang
- Anhui Fuqing Medical Equipment Co., Ltd., Hefei, China
| | - Bensheng Qiu
- Medical Imaging Center, Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China.
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11
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Tkotz K, Zeiger P, Hanspach J, Mathy CS, Laun FB, Uder M, Nagel AM, Gast LV. Parameter optimization for proton density fat fraction quantification in skeletal muscle tissue at 7 T. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01195-2. [PMID: 39105951 DOI: 10.1007/s10334-024-01195-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/16/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024]
Abstract
OBJECTIVE To establish an image acquisition and post-processing workflow for the determination of the proton density fat fraction (PDFF) in calf muscle tissue at 7 T. MATERIALS AND METHODS Echo times (TEs) of the applied vendor-provided multi-echo gradient echo sequence were optimized based on simulations of the effective number of signal averages (NSA*). The resulting parameters were validated by measurements in phantom and in healthy calf muscle tissue (n = 12). Additionally, methods to reduce phase errors arising at 7 T were evaluated. Finally, PDFF values measured at 7 T in calf muscle tissue of healthy subjects (n = 9) and patients with fatty replacement of muscle tissue (n = 3) were compared to 3 T results. RESULTS Simulations, phantom and in vivo measurements showed the importance of using optimized TEs for the fat-water separation at 7 T. Fat-water swaps could be mitigated using a phase demodulation with an additional B0 map, or by shifting the TEs to longer values. Muscular PDFF values measured at 7 T were comparable to measurements at 3 T in both healthy subjects and patients with increased fatty replacement. CONCLUSION PDFF determination in calf muscle tissue is feasible at 7 T using a chemical shift-based approach with optimized acquisition and post-processing parameters.
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Affiliation(s)
- Katharina Tkotz
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Paula Zeiger
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jannis Hanspach
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Claudius S Mathy
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Lena V Gast
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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12
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Stelter J, Weiss K, Steinhelfer L, Spieker V, Huaroc Moquillaza E, Zhang W, Makowski MR, Schnabel JA, Kainz B, Braren RF, Karampinos DC. Simultaneous whole-liver water T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ mapping with isotropic resolution during free-breathing. NMR IN BIOMEDICINE 2024:e5216. [PMID: 39099162 DOI: 10.1002/nbm.5216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 06/03/2024] [Accepted: 06/18/2024] [Indexed: 08/06/2024]
Abstract
PURPOSE To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specificT 1 $$ {\mathrm{T}}_1 $$ (wT 1 $$ {\mathrm{wT}}_1 $$ ) andT 2 $$ {\mathrm{T}}_2 $$ (wT 2 $$ {\mathrm{wT}}_2 $$ ) mapping for the characterization of diffuse and oncological liver diseases. METHODS The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve differentT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing forB 1 $$ {B}_1 $$ insensitivity at 3 T, to correct for relaxation-induced blurring, and to mapT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies. RESULTS Simulations demonstrate goodB 1 $$ {B}_1 $$ insensitivity of the proposed method in measuringT 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ values. The proposed method produces co-registeredwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ maps with a good agreement with reference methods (phantom:wT 1 = 1 . 02 wT 1,ref - 8 . 93 ms , R 2 = 0 . 991 $$ {\mathrm{wT}}_1=1.02\kern0.1em {\mathrm{wT}}_{1,\mathrm{ref}}-8.93\kern0.1em \mathrm{ms},{R}^2=0.991 $$ ;wT 2 = 1 . 03 wT 2,ref + 0 . 73 ms , R 2 = 0 . 995 $$ {\mathrm{wT}}_2=1.03\kern0.1em {\mathrm{wT}}_{2,\mathrm{ref}}+0.73\kern0.1em \mathrm{ms},{R}^2=0.995 $$ ). The proposedwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ mapping exhibits good repeatability and can be robustly performed in patients with pathologies. CONCLUSIONS The proposed method allows whole-liverwT 1 $$ {\mathrm{wT}}_1 $$ andwT 2 $$ {\mathrm{wT}}_2 $$ quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.
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Affiliation(s)
- Jonathan Stelter
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | | | - Lisa Steinhelfer
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Veronika Spieker
- Institute of Machine Learning for Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Elizabeth Huaroc Moquillaza
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Weitong Zhang
- Department of Computing, Imperial College London, London, United Kingdom
| | - Marcus R Makowski
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Julia A Schnabel
- Institute of Machine Learning for Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- School of Biomedical Imaging and Imaging Sciences, King's College London, London, United Kingdom
| | - Bernhard Kainz
- Department of Computing, Imperial College London, London, United Kingdom
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Rickmer F Braren
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, Germany
- Munich Data Science Institute, Technical University of Munich, Garching, Germany
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13
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Rabinowich A, Avisdris N, Yehuda B, Zilberman A, Graziani T, Neeman B, Specktor-Fadida B, Link-Sourani D, Wexler Y, Herzlich J, Krajden Haratz K, Joskowicz L, Ben Sira L, Hiersch L, Ben Bashat D. Fetal MRI-Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification. J Magn Reson Imaging 2024; 60:767-774. [PMID: 37982367 DOI: 10.1002/jmri.29141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/06/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification. HYPOTHESIS Smaller, leaner fetuses are malnourished and will experience unfavorable outcomes. STUDY TYPE Prospective. POPULATION 40 SGA fetuses, 26 (61.9%) females: 10/40 (25%) had obstetric interventions due to non-reassuring fetal status (NRFS), and 17/40 (42.5%) experienced adverse neonatal events (CANO). Participants underwent MRI between gestational ages 30 + 2 and 37 + 2. FIELD STRENGTH/SEQUENCE 3-T, True Fast Imaging with Steady State Free Precession (TruFISP) and T1-weighted two-point Dixon (T1W Dixon) sequences. ASSESSMENT Total body volume (TBV), fat signal fraction (FSF), and the fat-to-body volumes ratio (FBVR) were extracted from TruFISP and T1W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded. STATISTICAL TESTS Univariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. A P-value <0.05 was considered statistically significant. RESULTS FBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2-0.76) and FSF (OR 0.95, CI 0.91-0.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56-0.86) and FSF (OR 0.96, CI 0.93-0.99) were linked to CANO. The FBVR sensitivity/specificity for obstetric interventions was 85.7%/87.5%, and the TBV sensitivity/specificity for CANO was 82.35%/86.4%. The sonographic criteria sensitivity/specificity for obstetric interventions was 100%/33.3% and insignificant for CANO (P = 0.145). DATA CONCLUSION Reduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Aviad Rabinowich
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Netanell Avisdris
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Bossmat Yehuda
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Ayala Zilberman
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Tamir Graziani
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Bar Neeman
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Bella Specktor-Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dafna Link-Sourani
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Yair Wexler
- School of Neurobiology, Biochemistry and Biophysics, The George S. Wise Faculty of Life Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Jacky Herzlich
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Neonatal Intensive Care Unit, Dana Dwek Children's Hospital, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Karina Krajden Haratz
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Liat Ben Sira
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Liran Hiersch
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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14
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Froeling M, Heskamp L. The effect of fat model variation on muscle fat fraction quantification in a cross-sectional cohort. NMR IN BIOMEDICINE 2024:e5217. [PMID: 39077882 DOI: 10.1002/nbm.5217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/19/2024] [Accepted: 06/25/2024] [Indexed: 07/31/2024]
Abstract
Spectroscopic imaging, rooted in Dixon's two-echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low-fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left-right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left-right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset.
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Affiliation(s)
- Martijn Froeling
- Center for Image Sciences, Precision Imaging Group, Division Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Linda Heskamp
- Center for Image Sciences, Precision Imaging Group, Division Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
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15
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Qi H, Jiang S, Nan J, Guo H, Cheng C, He X, Jin H, Zhang R, Lei J. Application and research progress of magnetic resonance proton density fat fraction in metabolic dysfunction-associated steatotic liver disease: a comprehensive review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04448-9. [PMID: 39048719 DOI: 10.1007/s00261-024-04448-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 07/27/2024]
Abstract
Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), formerly known as Non-Alcoholic Fatty Liver Disease (NAFLD), is a chronic liver disorder associated with disturbances in lipid metabolism. The disease is prevalent worldwide, particularly closely linked with metabolic syndromes such as obesity and diabetes. Magnetic Resonance Proton Density Fat Fraction (MRI-PDFF), serving as a non-invasive and highly quantitative imaging assessment tool, holds promising applications in the diagnosis and research of MASLD. This paper aims to comprehensively review and summarize the applications and research progress of MRI-PDFF technology in MASLD, analyze its strengths and challenges, and anticipate its future developments in clinical practice.
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Affiliation(s)
- Hongyan Qi
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Chengguan District, Lanzhou City, 730000, Gansu Province, China
| | | | - Jiang Nan
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hang Guo
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Cai Cheng
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Xin He
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Hongyang Jin
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Rongfan Zhang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Chengguan District, Lanzhou City, 730000, Gansu Province, China.
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
- Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, Gansu, China.
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16
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Shih SF, Wu HH. Free-breathing MRI techniques for fat and R 2* quantification in the liver. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01187-2. [PMID: 39039272 DOI: 10.1007/s10334-024-01187-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/18/2024] [Accepted: 07/02/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVE To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities. MATERIALS AND METHODS This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification. RESULTS Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity. DISCUSSION As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.
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Affiliation(s)
- Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
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17
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Yang R, Peng H, Pan J, Wan Q, Zou C, Hu F. Native and Gd-EOB-DTPA-Enhanced T1 mapping for Assessment of Liver Fibrosis in NAFLD: Comparative Analysis of Modified Look-Locker Inversion Recovery and Water-specific T1 mapping. Acad Radiol 2024:S1076-6332(24)00443-4. [PMID: 39043516 DOI: 10.1016/j.acra.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/25/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate the diagnostic performance of water-specific T1 mapping for staging liver fibrosis in a non-alcoholic fatty liver disease (NAFLD) rabbit model, in comparison to Modified Look-Locker Inversion recovery (MOLLI) T1 mapping. MATERIALS AND METHODS 60 rabbits were randomly divided into the control group (12 rabbits) and NAFLD model groups (eight rabbits per subgroup) corresponding to different durations of high-fat high cholesterol diet feeding. All rabbits underwent MRI examination including MOLLI T1 mapping and 3D multi-echo variable flip angle (VFAME- GRE) sequences were acquired before and 20 min after the administration of Gd- EOB-DTPA. Histological assessments were performed to evaluate steatosis, inflammation, ballooning, and fibrosis. Statistical analysis included the intraclass correlation coefficient, analysis of variance, spearman correlation, multiple linear regression, and receiver operating characteristic curve. RESULTS A moderate correlation was observed between conventional native T1 and MRI-PDFF (r = -0.513, P < 0.001), as well as between conventional native T1 and liver steatosis grades (r = -0.319, P = 0.016). However, no significant correlation was found between the native wT1 and PDFF (r = 0.137, P = 0.314), or between the native wT1 and steatosis grades (r = 0.106, P = 0.435). In the multiple regression analysis, liver fibrosis, and hepatocellular ballooning were identified as independent factors influencing native wT1 in this study (R2 =0.545, P < 0.05), while steatosis was independently associated with conventional native T1 (R2 =0.321, P < 0.05). The AUC values for native T1, native wT1, HBP T1, and HBP wT1 were 0.549(0.410-0.682), 0.811(0.684-0.903), 0.775(0.644-0.876), and 0.752(0.619-0.858) for F1 or higher, 0.581(0.441-0.711), 0.828(0.704-0.916), 0.832(0.708-0.919), and 0.854(0.734-0.934) for F2 or higher, respectively. CONCLUSION The native wT1 may provide a more reliable assessment of early liver fibrosis in the context of NAFLD compared to conventional native T1.
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Affiliation(s)
- Ru Yang
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, No.278, Baoguang Road, Xindu District, Chengdu, Sichuan, China (R.Y., J.P., F.H.)
| | - Hao Peng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, China (H.P., Q.W., C.Z.)
| | - Jing Pan
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, No.278, Baoguang Road, Xindu District, Chengdu, Sichuan, China (R.Y., J.P., F.H.)
| | - Qian Wan
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, China (H.P., Q.W., C.Z.)
| | - Chao Zou
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong, China (H.P., Q.W., C.Z.)
| | - Fubi Hu
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, No.278, Baoguang Road, Xindu District, Chengdu, Sichuan, China (R.Y., J.P., F.H.).
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Katsumata S, Takahashi T, Nishimura S, Hondera T, Yasuda M, Kato K. [Basic MRI Study in the Imaging Direction of a Diffusion-weighted Whole Body Imaging with Background Body Signal Suppression]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:741-749. [PMID: 38866536 DOI: 10.6009/jjrt.2024-1449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
A diffusion-weighted whole body imaging with background body signal suppression (DWIBS) is usually imaged as a whole body with Transverse (Tra). However, Tra has a large number of stations and a larger number than Coronal (Cor), so the scan time is longer. There are also drawbacks, such as signal unevenness between series. It is known that the effect of distortion is large in Cor. There is no report on it in Sagittal (Sag). Therefore, in this study, we focused on Sag and examined the imaging time, image distortion, fat suppression effect, and continuity between stations. In the examination by the phantom, the scan time was the shortest for Cor and the longest for Sag. In the strain evaluation, the effect of strain could be suppressed compared to Cor by using a rectangle field of view (FOV) in the anterior to posterior (AP) direction in Tra and Sag. There was no difference in the fat suppression effect depending on the imaging direction. Similar results were obtained in a study of 10 healthy volunteers, with Sag having the best continuity between stations.
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Affiliation(s)
- Shota Katsumata
- Department of Radiological Technology, Showa University Hospital
| | | | - Shuko Nishimura
- Department of Radiological Technology, Showa University Koto Toyosu Hospital
| | - Tetsuichi Hondera
- Department of Radiological Technology, Showa University Hospital
- Showa University Graduate School of Health Sciences
| | - Mitsuyoshi Yasuda
- Department of Radiological Technology, Showa University Hospital
- Showa University Graduate School of Health Sciences
| | - Kyoichi Kato
- Showa University Graduate School of Health Sciences
- Showa University Radiological Technology
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Nosrati R, Calakli F, Afacan O, Pelkola K, Nichols R, Connaughton P, Bedoya MA, Tsai A, Bixby S, Warfield SK. Free-Breathing High-Resolution, Swap-Free, and Motion-Corrected Water/Fat Separation in Pediatric Abdominal MRI. Invest Radiol 2024:00004424-990000000-00221. [PMID: 38857418 DOI: 10.1097/rli.0000000000001092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
OBJECTIVES The T1-weighted GRE (gradient recalled echo) sequence with the Dixon technique for water/fat separation is an essential component of abdominal MRI (magnetic resonance imaging), useful in detecting tumors and characterizing hemorrhage/fat content. Unfortunately, the current implementation of this sequence suffers from several problems: (1) low resolution to maintain high pixel bandwidth and minimize chemical shift; (2) image blurring due to respiratory motion; (3) water/fat swapping due to the natural ambiguity between fat and water peaks; and (4) off-resonance fat blurring due to the multipeak nature of the fat spectrum. The goal of this study was to evaluate the image quality of water/fat separation using a high-resolution 3-point Dixon golden angle radial acquisition with retrospective motion compensation and multipeak fat modeling in children undergoing abdominal MRI. MATERIALS AND METHODS Twenty-two pediatric patients (4.2 ± 2.3 years) underwent abdominal MRI on a 3 T scanner with routine abdominal protocol and with a 3-point Dixon radial-VIBE (volumetric interpolated breath-hold examination) sequence. Field maps were calculated using 3D graph-cut optimization followed by fat and water calculation from k-space data by iteratively solving an optimization problem. A 6-peak fat model was used to model chemical shifts in k-space. Residual respiratory motion was corrected through soft-gating by weighting each projection based on the estimated respiratory motion from the center of the k-space. Reconstructed images were reviewed by 3 pediatric radiologists on a PACS (picture archiving and communication systems) workstation. Subjective image quality and water/fat swapping artifact were scored by each pediatric radiologist using a 5-point Likert scale. The VoL (variance of Laplacian) of the reconstructed images was used to objectively quantify image sharpness. RESULTS Based on the overall Likert scores, the images generated using the described method were significantly superior to those reconstructed by the conventional 2-point Dixon technique (P < 0.05). Water/fat swapping artifact was observed in 14 of 22 patients using 2-point Dixon, and this artifact was not present when using the proposed method. Image sharpness was significantly improved using the proposed framework. CONCLUSIONS In smaller patients, a high-quality water/fat separation with sharp visualization of fine details is critical for diagnostic accuracy. High-resolution golden angle radial-VIBE 3-point Dixon acquisition with 6-peak fat model and soft-gated motion correction offers improved image quality at the expense of an additional ~1-minute acquisition time. Thus, this technique offers the potential to replace the conventional 2-point Dixon technique.
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Affiliation(s)
- Reyhaneh Nosrati
- From the Department of Radiology, Boston Children's Hospital, Boston, MA (R. Nosrati, F.C., O.A., K.P., R. Nichols, P.C., M.A.B., A.T., S.B., S.K.W.); and Harvard Medical School, Boston, MA (R. Nosrati, F.C., O.A., K.P., R. Nichols, P.C., M.A.B., A.T., S.B., S.K.W.)
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20
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Zheng G, Wei F, Lu P, Yang G, Li C, Lin C, Zhou Y, Chen Y, Tian J, Wang X, Wang L, Liu W, Zhang G, Cai Q, Huang H, Yun Y. IDEAL-IQ measurement can distinguish dysplastic nodule from early hepatocellular carcinoma: a case-control study. Quant Imaging Med Surg 2024; 14:3901-3913. [PMID: 38846285 PMCID: PMC11151266 DOI: 10.21037/qims-23-1593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/25/2024] [Indexed: 06/09/2024]
Abstract
Background Previous studies have confirmed that malignant transformation of dysplastic nodule (DN) into hepatocellular carcinoma (HCC) is accompanied by reduction of iron content in nodules. This pathological abnormality can serve as the basis for magnetic resonance imaging (MRI). This study was designed to identify the feasibility of iterative decomposition of water and fat with echo asymmetry and least squares estimation-iron quantitative (IDEAL-IQ) measurement to distinguish early hepatocellular carcinoma (eHCC) from DN. Methods We reviewed MRI studies of 35 eHCC and 23 DN lesions (46 participants with 58 lesions total, 37 males, 9 females, 31-80 years old). The exams include IDEAL-IQ sequence and 3.0T MR conventional scan [including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and Gadopentic acid (Gd-GDPA)-enhanced]. Then, 3 readers independently diagnosed eHCC, DN, or were unable to distinguish eHCC from DN using conventional MRI (CMRI), and then assessed R2* value of nodules [R2* value represents the nodule iron content (NIC)] and R2* value of liver background [R2* value represents the liver background iron content (LBIC)] with IDEAL-IQ. Statistical analysis was conducted using the t-test for comparison of means, the Mann-Whitney test for comparison of medians, the chi-square test for comparison of frequencies, and diagnostic efficacy was evaluated by using receiver operating characteristic (ROC) curve. Results This study evaluated 35 eHCC participants (17 males, 6 females, 34-81 years old, nodule size: 10.5-27.6 mm, median 18.0 mm) and 23 DN participants (20 males, 3 females, 31-76 years old, nodule size: 16.30±4.095 mm). The NIC and ratio of NIC to LIBC (NIC/LBIC) of the eHCC group (35.926±12.806 sec-1, 0.327±0.107) was lower than that of the DN group (176.635±87.686 sec-1, 1.799±0.629) (P<0.001). Using NIC and NIC/LBIC to distinguish eHCC from DN, the true positive/false positive rates were 91.3%/94.3% and 87.0%/97.1%, respectively. The rates of CMRI, NIC and NIC/LBIC in diagnosis of eHCC were 77.1%, and 94.3%, 97.1%, respectively, and those of DN were 65.2%, 91.3%, and 87.0%, respectively. The diagnosis rate of eHCC and DN by CMRI was lower than that of NIC and NIC/LBIC (eHCC: P=0.03, 0.04, DN: P=0.02, 0.04). Conclusions Using IDEAL-IQ measurement can distinguish DN from eHCC.
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Affiliation(s)
- Guangping Zheng
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Fangjun Wei
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Puxuan Lu
- Department of Radiology, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Gendong Yang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Cuizu Li
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Chunming Lin
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yun Zhou
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yixin Chen
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Jianing Tian
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Xiaolei Wang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Linjing Wang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Wenhao Liu
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Guangfeng Zhang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Qingxian Cai
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Hua Huang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yongxing Yun
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
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Wan C, He W, Xu Z. Water-Fat Separation for the Knee on a 50 mT Portable MRI Scanner. IEEE Trans Biomed Eng 2024; 71:1687-1696. [PMID: 38150336 DOI: 10.1109/tbme.2023.3347441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
OBJECTIVE The Dixon method is frequently employed in clinical and scientific research for fat suppression, because it has lower sensitivity to static magnetic field inhomogeneity compared to chemical shift selective saturation or its variants and maintains image signal-to-noise ratio (SNR). Recently, research on very-low-field (VLF < 100 mT) magnetic resonance imaging (MRI) has regained popularity. However, there is limited literature on water-fat separation in VLF MRI. Here, we present a modified two-point Dixon method specifically designed for VLF MRI. METHODS Most experiments were performed on a homemade 50 mT portable MRI scanner. The receiving coil adopted a homemade quadrature receiving coil. The data were acquired using spin-echo and gradient-echo sequences. We considered the T2* effect, and added priori information to existing two-point Dixon method. Then, the method used regional iterative phasor extraction (RIPE) to extract the error phasor. Finally, least squares solutions for water and fat were obtained and fat signal fraction was calculated. RESULTS For phantom evaluation, water-only and fat-only images were obtained and the local fat signal fractions were calculated, with two samples being 0.94 and 0.93, respectively. For knee imaging, cartilage, muscle and fat could be clearly distinguished. The water-only images were able to highlight areas such as cartilage that could not be easily distinguished without separation. CONCLUSION This work has demonstrated the feasibility of using a 50 mT MRI scanner for water-fat separation. SIGNIFICANCE To the best of our knowledge, this is the first reported result of water-fat separation at a 50 mT portable MRI scanner.
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Tamada D, van der Heijden RA, Weaver J, Hernando D, Reeder SB. Confidence maps for reliable estimation of proton density fat fraction and R 2 * in the liver. Magn Reson Med 2024; 91:2172-2187. [PMID: 38174431 PMCID: PMC10950533 DOI: 10.1002/mrm.29986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/31/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. METHODS Confidence maps for both PDFF andR 2 * $$ {R}_2^{\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF andR 2 * $$ {R}_2^{\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements. RESULTS Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF andR 2 * $$ {R}_2^{\ast } $$ maps, as identified by confidence maps. CONCLUSION A proposed confidence map algorithm that identifies reliable areas of PDFF andR 2 * $$ {R}_2^{\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.
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Affiliation(s)
- Daiki Tamada
- Departments of Radiology, University of Wisconsin-Madison, Madison
| | - Rianne A. van der Heijden
- Departments of Radiology, University of Wisconsin-Madison, Madison
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jayse Weaver
- Departments of Medical Physics, University of Wisconsin-Madison, Madison
| | - Diego Hernando
- Departments of Radiology, University of Wisconsin-Madison, Madison
- Departments of Medical Physics, University of Wisconsin-Madison, Madison
| | - Scott B Reeder
- Departments of Radiology, University of Wisconsin-Madison, Madison
- Departments of Medical Physics, University of Wisconsin-Madison, Madison
- Departments of Biomedcal Engineering, University of Wisconsin-Madison, Madison
- Departments of Medicine, University of Wisconsin-Madison, Madison
- Departments of Emergency Medicine, University of Wisconsin-Madison, Madison, WI
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Eisen CK, Liebig P, Herrler J, Ritter D, Lévy S, Uder M, Nagel AM, Grodzki D. Fast online spectral-spatial pulse design for subject-specific fat saturation in cervical spine and foot imaging at 1.5 T. MAGMA (NEW YORK, N.Y.) 2024; 37:257-272. [PMID: 38366129 PMCID: PMC10995033 DOI: 10.1007/s10334-024-01149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE To compensate subject-specific field inhomogeneities and enhance fat pre-saturation with a fast online individual spectral-spatial (SPSP) single-channel pulse design. METHODS The RF shape is calculated online using subject-specific field maps and a predefined excitation k-space trajectory. Calculation acceleration options are explored to increase clinical viability. Four optimization configurations are compared to a standard Gaussian spectral selective pre-saturation pulse and to a Dixon acquisition using phantom and volunteer (N = 5) data at 1.5 T with a turbo spin echo (TSE) sequence. Measurements and simulations are conducted across various body parts and image orientations. RESULTS Phantom measurements demonstrate up to a 3.5-fold reduction in residual fat signal compared to Gaussian fat saturation. In vivo evaluations show improvements up to sixfold for dorsal subcutaneous fat in sagittal cervical spine acquisitions. The versatility of the tailored trajectory is confirmed through sagittal foot/ankle, coronal, and transversal cervical spine experiments. Additional measurements indicate that excitation field (B1) information can be disregarded at 1.5 T. Acceleration methods reduce computation time to a few seconds. DISCUSSION An individual pulse design that primarily compensates for main field (B0) inhomogeneities in fat pre-saturation is successfully implemented within an online "push-button" workflow. Both fat saturation homogeneity and the level of suppression are improved.
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Affiliation(s)
- Christian Karl Eisen
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Patrick Liebig
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Jürgen Herrler
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Dieter Ritter
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin Michael Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Grodzki
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
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Michelotti FC, Kupriyanova Y, Mori T, Küstner T, Heilmann G, Bombrich M, Möser C, Schön M, Kuss O, Roden M, Schrauwen-Hinderling VB. An Empirical Approach to Derive Water T 1 from Multiparametric MR Images Using an Automated Pipeline and Comparison With Liver Stiffness. J Magn Reson Imaging 2024; 59:1193-1203. [PMID: 37530755 DOI: 10.1002/jmri.28906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Water T1 of the liver has been shown to be promising in discriminating the progressive forms of fatty liver diseases, inflammation, and fibrosis, yet proper correction for iron and lipid is required. PURPOSE To examine the feasibility of an empirical approach for iron and lipid correction when measuring imaging-based T1 and to validate this approach by spectroscopy on in vivo data. STUDY TYPE Retrospective. POPULATION Next to mixed lipid-iron phantoms, individuals with different hepatic lipid content were investigated, including people with type 1 diabetes (N = 15, %female = 15.6, age = 43.5 ± 14.0), or type 2 diabetes mellitus (N = 21, %female = 28.9, age = 59.8 ± 9.7) and healthy volunteers (N = 9, %female = 11.1, age = 58.0 ± 8.1). FIELD STRENGTH/SEQUENCES 3 T, balanced steady-state free precession MOdified Look-Locker Inversion recovery (MOLLI), multi- and dual-echo gradient echo Dixon, gradient echo magnetic resonance elastography (MRE). ASSESSMENT T1 values were measured in phantoms to determine the respective correction factors. The correction was tested in vivo and validated by proton magnetic resonance spectroscopy (1 H-MRS). The quantification of liver T1 based on automatic segmentation was compared to the T1 values based on manual segmentation. The association of T1 with MRE-derived liver stiffness was evaluated. STATISTICAL TESTS Bland-Altman plots and intraclass correlation coefficients (ICCs) were used for MOLLI vs. 1 H-MRS agreement and to compare liver T1 values from automatic vs. manual segmentation. Pearson's r correlation coefficients for T1 with hepatic lipids and liver stiffness were determined. A P-value of 0.05 was considered statistically significant. RESULTS MOLLI T1 values after correction were found in better agreement with the 1 H-MRS-derived water T1 (ICC = 0.60 [0.37; 0.76]) in comparison with the uncorrected T1 values (ICC = 0.18 [-0.09; 0.44]). Automatic quantification yielded similar liver T1 values (ICC = 0.9995 [0.9991; 0.9997]) as with manual segmentation. A significant correlation of T1 with liver stiffness (r = 0.43 [0.11; 0.67]) was found. A marked and significant reduction in the correlation strength of T1 with liver stiffness (r = 0.05 [-0.28; 0.38], P = 0.77) was found after correction for hepatic lipid content. DATA CONCLUSION Imaging-based correction factors enable accurate estimation of water T1 in vivo. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Filippo C Michelotti
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Yuliya Kupriyanova
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Tim Mori
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Thomas Küstner
- Diagnostics and Interventional Radiology, Medical Image and Data Analysis (MIDAS.lab), University Hospital of Tübingen, Tübingen, Germany
| | - Geronimo Heilmann
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Maria Bombrich
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Clara Möser
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Oliver Kuss
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Centre for Health and Society, Faculty of Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Vera B Schrauwen-Hinderling
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
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Ishimaru H, Ikebe Y, Izumo T, Imai H, Morikawa M, Ideguchi R, Ishiyama A, Koike H, Uetani M, Toya R. Assessment for Carotid Atherosclerotic Plaque Using Vessel Wall Magnetic Resonance Imaging: A Multireader ROC Study to Determine Optimal Sequence for Detecting Vessel Wall Calcification. J Vasc Res 2024; 61:122-128. [PMID: 38547846 DOI: 10.1159/000538175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/27/2024] [Indexed: 06/05/2024] Open
Abstract
INTRODUCTION We aimed to compare conventional vessel wall MR imaging techniques and quantitative susceptibility mapping (QSM) to determine the optimal sequence for detecting carotid artery calcification. METHODS Twenty-two patients who underwent carotid vessel wall MR imaging and neck CT were enrolled. Four slices of 6-mm sections from the bilateral internal carotid bifurcation were subdivided into 4 segments according to clock position (0-3, 3-6, 6-9, and 9-12) and assessed for calcification. Two blinded radiologists independently reviewed a total of 704 segments and scored the likelihood of calcification using a 5-point scale on spin-echo imaging, FLASH, and QSM. The observer performance for detecting calcification was evaluated by a multireader, multiple-case receiver operating characteristic study. Weighted κ statistics were calculated to assess interobserver agreement. RESULTS QSM had a mean area under the receiver operating characteristic curve of 0.85, which was significantly higher than that of any other sequence (p < 0.01) and showed substantial interreader agreement (κ = 0.68). A segment with a score of 3-5 was defined as positive, and a segment with a score of 1-2 was defined as negative; the sensitivity and specificity of QSM were 0.75 and 0.87, respectively. CONCLUSION QSM was the most reliable MR sequence for the detection of plaque calcification.
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Affiliation(s)
- Hideki Ishimaru
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Yohei Ikebe
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Tsuyoshi Izumo
- Department of Neurolosurgery, Nagasaki University Hospital, Nagasaki, Japan
| | - Hiroshi Imai
- MR Research and Collaboration, Siemens Healthcare K.K, Osaki, Shinagawa, Tokyo, Japan
| | - Minoru Morikawa
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Reiko Ideguchi
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Ayano Ishiyama
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hirofumi Koike
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Masataka Uetani
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Ryo Toya
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Brandhorst S, Levine ME, Wei M, Shelehchi M, Morgan TE, Nayak KS, Dorff T, Hong K, Crimmins EM, Cohen P, Longo VD. Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk. Nat Commun 2024; 15:1309. [PMID: 38378685 PMCID: PMC10879164 DOI: 10.1038/s41467-024-45260-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 01/18/2024] [Indexed: 02/22/2024] Open
Abstract
In mice, periodic cycles of a fasting mimicking diet (FMD) protect normal cells while killing damaged cells including cancer and autoimmune cells, reduce inflammation, promote multi-system regeneration, and extend longevity. Here, we performed secondary and exploratory analysis of blood samples from a randomized clinical trial (NCT02158897) and show that 3 FMD cycles in adult study participants are associated with reduced insulin resistance and other pre-diabetes markers, lower hepatic fat (as determined by magnetic resonance imaging) and increased lymphoid to myeloid ratio: an indicator of immune system age. Based on a validated measure of biological age predictive of morbidity and mortality, 3 FMD cycles were associated with a decrease of 2.5 years in median biological age, independent of weight loss. Nearly identical findings resulted from a second clinical study (NCT04150159). Together these results provide initial support for beneficial effects of the FMD on multiple cardiometabolic risk factors and biomarkers of biological age.
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Affiliation(s)
- Sebastian Brandhorst
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Morgan E Levine
- Department of Pathology, Yale School of Medicine, New Haven, CT, 06519, USA
| | - Min Wei
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Mahshid Shelehchi
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Todd E Morgan
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Tanya Dorff
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Kurt Hong
- Center of Clinical Nutrition and Applied Health Research, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA
| | - Eileen M Crimmins
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
- Center on Biodemography and Population Health, University of California Los Angeles and University of Southern California, Los Angeles, CA, 90089, USA
| | - Pinchas Cohen
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA
| | - Valter D Longo
- Longevity Institute, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, 90089, USA.
- AIRC Institute of Molecular Oncology, Italian Foundation for Cancer Research Institute of Molecular Oncology, 20139, Milan, Italy.
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Hollý S, Chmelík M, Suchá S, Suchý T, Beneš J, Pátrovič L, Juskanič D. Photon-counting CT using multi-material decomposition algorithm enables fat quantification in the presence of iron deposits. Phys Med 2024; 118:103210. [PMID: 38219560 DOI: 10.1016/j.ejmp.2024.103210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 11/29/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024] Open
Abstract
PURPOSE A new generation of CT detectors were recently developed with the ability to measure individual photon's energy and thus provide spectral information. The aim of this work was to assess the performance of simultaneous fat and iron quantification using a clinical photon-counting CT (PCCT) and its comparison to dual-energy CT (DECT), MRS and MRI at 3 T. METHODS Two 3D printed cylindrical phantoms with 32 samples (n = 12 fat fractions between 0 % and 100 %, n = 20 with mixtures of fat and iron) were scanned with PCCT and DECT scanners for comparison. A three-material decomposition approach was used to estimate the volume fractions of fat (FF), iron and soft tissue. The same phantoms were examined by MRI (6-echo DIXON, a.k.a. Q-DIXON) and MRS (multi-echo STEAM, a.k.a. HISTO) at 3 T for comparison. RESULTS PCCT, DECT, MRI and MRS computed FFs showed correlation with reference fat fraction values in samples with no iron (r > 0.98). PCCT decomposition showed slightly weaker correlation with FFref in samples with added iron (r = 0.586) compared to MRI (r = 0.673) and MRS (r = 0.716) methods. On the other hand, it showed no systematic over- or underestimation. Surprisingly, DECT decomposition-derived FF showed strongest correlation (r = 0.758) in these samples, however systematic overestimation was observed. FF values computed by three-material PCCT decomposition, DECT decomposition, MRI and MRS were unaffected by iron concentration. CONCLUSIONS This in-vitro study shows for the first time that photon-counting computed tomography may be used for quantification of fat content in the presence of iron deposits.
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Affiliation(s)
- Samuel Hollý
- JESSENIUS - diagnostic center, Nitra, Slovakia; Institute of Biophysics and Informatics, First Faculty of Medicine Charles University, Prague, Czech Republic
| | - Marek Chmelík
- JESSENIUS - diagnostic center, Nitra, Slovakia; Department of Technical Disciplines in Health Care, Faculty of Health Care, University of Prešov, Slovakia.
| | - Slavomíra Suchá
- Department of Technical Disciplines in Health Care, Faculty of Health Care, University of Prešov, Slovakia
| | - Tomáš Suchý
- Department of Technical Disciplines in Health Care, Faculty of Health Care, University of Prešov, Slovakia
| | - Jiři Beneš
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | | | - Dominik Juskanič
- JESSENIUS - diagnostic center, Nitra, Slovakia; Medical Faculty, Commenius University in Bratislava, Slovakia
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Tian Y, Nayak KS. Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling. Magn Reson Med 2024; 91:649-659. [PMID: 37815020 PMCID: PMC10841523 DOI: 10.1002/mrm.29885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/23/2023] [Accepted: 09/21/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T. METHODS A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility. RESULTS We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast. CONCLUSIONS We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.
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Affiliation(s)
- Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Krishna S. Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
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Jerban S, Jang H, Chang EY, Bukata S, Du J, Chung CB. Bone Biomarkers Based on Magnetic Resonance Imaging. Semin Musculoskelet Radiol 2024; 28:62-77. [PMID: 38330971 DOI: 10.1055/s-0043-1776431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Magnetic resonance imaging (MRI) is increasingly used to evaluate the microstructural and compositional properties of bone. MRI-based biomarkers can characterize all major compartments of bone: organic, water, fat, and mineral components. However, with a short apparent spin-spin relaxation time (T2*), bone is invisible to conventional MRI sequences that use long echo times. To address this shortcoming, ultrashort echo time MRI sequences have been developed to provide direct imaging of bone and establish a set of MRI-based biomarkers sensitive to the structural and compositional changes of bone. This review article describes the MRI-based bone biomarkers representing total water, pore water, bound water, fat fraction, macromolecular fraction in the organic matrix, and surrogates for mineral density. MRI-based morphological bone imaging techniques are also briefly described.
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Affiliation(s)
- Saeed Jerban
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Hyungseok Jang
- Department of Radiology, University of California, San Diego, La Jolla, California
| | - Eric Y Chang
- Department of Radiology, University of California, San Diego, La Jolla, California
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, California
| | - Susan Bukata
- Department of Orthopaedic Surgery, University of California, San Diego, La Jolla, California
| | - Jiang Du
- Department of Radiology, University of California, San Diego, La Jolla, California
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, California
- Department of Bioengineering, University of California, San Diego, La Jolla, California
| | - Christine B Chung
- Department of Radiology, University of California, San Diego, La Jolla, California
- Research Service, Veterans Affairs San Diego Healthcare System, San Diego, California
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30
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Daudé P, Roussel T, Troalen T, Viout P, Hernando D, Guye M, Kober F, Confort Gouny S, Bernard M, Rapacchi S. Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging. Magn Reson Med 2024; 91:741-759. [PMID: 37814776 DOI: 10.1002/mrm.29860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ quantification using an open-source multi-language toolbox. METHODS Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B0 off-resonance, SNR and TEs. Experimental evaluation was conducted using calibrated fat-water phantoms acquired at 3T and multi-site open-source phantoms data. Algorithms' performances were observed on challenging in vivo datasets at 3T. Finally, reconstruction algorithms were investigated with different fat spectra to evaluate the importance of the fat model. RESULTS In silico and in vitro results proved most algorithms to be not sensitive to fat-water swaps andB 0 $$ {\mathrm{B}}_0 $$ offsets with five or more echoes. However, two methods remained inaccurate even with seven echoes and SNR = 50, and two other algorithms' precision depended on the echo spacing scheme (p < 0.05). The remaining four algorithms provided reliable performances with limits of agreement under 2% for PDFF and 6 s-1 forR 2 * $$ {R}_2^{\ast } $$ . The choice of fat spectrum model influenced quantification of PDFF mildly (<2% bias) and ofR 2 * $$ {R}_2^{\ast } $$ more severely, with errors up to 20 s-1 . CONCLUSION In promoting standardized comparisons of MRI-based fat and iron quantification using chemical-shift encoded multi-echo methods, this benchmark work has revealed some discrepancies between recent approaches for PDFF andR 2 * $$ {R}_2^{\ast } $$ mapping. Explicit choices and parameterization of the fat-water algorithm appear necessary for reproducibility. This open-source toolbox further enables the user to optimize acquisition parameters by predicting algorithms' margins of errors.
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Affiliation(s)
- Pierre Daudé
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tangi Roussel
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | | | - Patrick Viout
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Diego Hernando
- Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Maxime Guye
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Frank Kober
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Sylviane Confort Gouny
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Monique Bernard
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Stanislas Rapacchi
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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Kocaoglu M, Pednekar A, Fleck RJ, Dillman JR. Cardiothoracic Magnetic Resonance Angiography. Curr Probl Diagn Radiol 2024; 53:154-165. [PMID: 37891088 DOI: 10.1067/j.cpradiol.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/01/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
Catheter-based angiography is regarded as the clinical reference imaging technique for vessel imaging; however, it is invasive and is currently used for intervention or physiologic measurements. Contrast enhanced magnetic resonance angiography (MRA) with gadolinium-based contrast agents can be performed as a three-dimensional (3D) MRA or as a time resolved 3D (4D) MRA without physiologic synchronization, in which case cardiac and respiratory motion may blur the edges of the vessels and cardiac chambers. Ferumoxytol has recently been a popular contrast agent for MRA in patients with chronic renal failure. Noncontrast 3D MRA with ECG gating and respiratory navigation are safe and accurate noninvasive cross-sectional imaging techniques for the visualization of great vessels of the heart and coronary arteries in a variety of cardiovascular disorders including complex congenital heart diseases. Noncontrast flow dependent MRA techniques such as time of flight, phase contrast, and black-blood MRA techniques can be used as complementary or primary techniques. Here we review both conventional and relatively new contrast enhanced and non-contrast enhanced MRA techniques including ferumoxytol enhanced MRA, and bright-blood and water-fat separation based noncontrast 3D MRA techniques.
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Affiliation(s)
- Murat Kocaoglu
- Department of Radiology, Cincinnati Children's Hospital Medical Center, MLC1 5031, 3333 Burnet Ave, Cincinnati, OH 45229, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Amol Pednekar
- Department of Radiology, Cincinnati Children's Hospital Medical Center, MLC1 5031, 3333 Burnet Ave, Cincinnati, OH 45229, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Robert J Fleck
- Department of Radiology, Cincinnati Children's Hospital Medical Center, MLC1 5031, 3333 Burnet Ave, Cincinnati, OH 45229, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, MLC1 5031, 3333 Burnet Ave, Cincinnati, OH 45229, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Orcel T, Chau HT, Turlin B, Chaigneau J, Bannier E, Otal P, Frampas E, Leguen A, Boulic A, Saint-Jalmes H, Aubé C, Boursier J, Bardou-Jacquet E, Gandon Y. Evaluation of proton density fat fraction (PDFF) obtained from a vendor-neutral MRI sequence and MRQuantif software. Eur Radiol 2023; 33:8999-9009. [PMID: 37402003 DOI: 10.1007/s00330-023-09798-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/29/2023] [Accepted: 04/21/2023] [Indexed: 07/05/2023]
Abstract
OBJECTIVE To validate the proton density fat fraction (PDFF) obtained by the MRQuantif software from 2D chemical shift encoded MR (CSE-MR) data in comparison with the histological steatosis data. METHODS This study, pooling data from 3 prospective studies spread over time between January 2007 and July 2020, analyzed 445 patients who underwent 2D CSE-MR and liver biopsy. MR derived liver iron concentration (MR-LIC) and PDFF was calculated using the MRQuantif software. The histological standard steatosis score (SS) served as reference. In order to get a value more comparable to PDFF, histomorphometry fat fraction (HFF) were centrally determined for 281 patients. Spearman correlation and the Bland and Altman method were used for comparison. RESULTS Strong correlations were found between PDFF and SS (rs = 0.84, p < 0.001) or HFF (rs = 0.87, p < 0.001). Spearman's coefficients increased to 0.88 (n = 324) and 0.94 (n = 202) when selecting only the patients without liver iron overload. The Bland and Altman analysis between PDFF and HFF found a mean bias of 5.4% ± 5.7 [95% CI 4.7, 6.1]. The mean bias was 4.7% ± 3.7 [95% CI 4.2, 5.3] and 7.1% ± 8.8 [95% CI 5.2, 9.0] for the patients without and with liver iron overload, respectively. CONCLUSION The PDFF obtained by MRQuantif from a 2D CSE-MR sequence is highly correlated with the steatosis score and very close to the fat fraction estimated by histomorphometry. Liver iron overload reduced the performance of steatosis quantification and joint quantification is recommended. This device-independent method can be particularly useful for multicenter studies. CLINICAL RELEVANCE STATEMENT The quantification of liver steatosis using a vendor-neutral 2D chemical-shift MR sequence, processed by MRQuantif, is well correlated to steatosis score and histomorphometric fat fraction obtained from biopsy, whatever the magnetic field and the MR device used. KEY POINTS • The PDFF measured by MRQuantif from 2D CSE-MR sequence data is highly correlated to hepatic steatosis. • Steatosis quantification performance is reduced in case of significant hepatic iron overload. • This vendor-neutral method may allow consistent estimation of PDFF in multicenter studies.
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Affiliation(s)
- T Orcel
- Department of Radiology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
| | - H T Chau
- Department of Radiology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
- NUMECAN, INSERM U1099, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
| | - B Turlin
- NUMECAN, INSERM U1099, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
- Department of Pathology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
| | - J Chaigneau
- HIFIH, UPRES EA3859, Angers University Hospital, 4 Rue Larrey, 49993, Angers, France
| | - E Bannier
- Department of Radiology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
- EMPENN U746 Unit/Project, INSERM/INRIA, IRISA, University of Rennes, Beaulieu Campus, UMR CNRS 6074, 35042, Rennes, France
| | - P Otal
- Department of Radiology, Toulouse University Hospital, 1 Av Pr J. Poulhes, 31059, Toulouse, France
| | - E Frampas
- Department of Radiology, Nantes University Hospital, 1 Pl. Alexis-Ricordeau, 44000, Nantes, France
| | - A Leguen
- Department of Radiology, Bretagne-Atlantique Hospital, 20 Bd Général Maurice Guillaudot, 56000, Vannes, France
| | - A Boulic
- Department of Radiology, Bretagne Sud Hospital, 5 Avenue de Choiseul, 56322, Lorient, France
| | - H Saint-Jalmes
- INSERM U1099, LTSI, University of Rennes, Beaulieu Campus, 35042, Rennes, France
| | - C Aubé
- HIFIH, UPRES EA3859, Angers University Hospital, 4 Rue Larrey, 49993, Angers, France
- Department of Radiology, Angers University Hospital, 4 Rue Larrey, 49993, Angers, France
| | - J Boursier
- HIFIH, UPRES EA3859, Angers University Hospital, 4 Rue Larrey, 49993, Angers, France
- Department of Hepatology-GastoeEnterology, Angers University Hospital, 4 Rue Larrey, 49993, Angers, France
| | - E Bardou-Jacquet
- NUMECAN, INSERM U1099, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
- Department of Hepatology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France
| | - Y Gandon
- Department of Radiology, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France.
- NUMECAN, INSERM U1099, Rennes University Hospital, 2 Rue H. Le Guilloux, 35033, Rennes, France.
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Rabinowich A, Avisdris N, Zilberman A, Link-Sourani D, Lazar S, Herzlich J, Specktor-Fadida B, Joskowicz L, Malinger G, Ben-Sira L, Hiersch L, Ben Bashat D. Reduced adipose tissue in growth-restricted fetuses using quantitative analysis of magnetic resonance images. Eur Radiol 2023; 33:9194-9202. [PMID: 37389606 DOI: 10.1007/s00330-023-09855-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 04/18/2023] [Accepted: 04/21/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVES Fat-water MRI can be used to quantify tissues' lipid content. We aimed to quantify fetal third trimester normal whole-body subcutaneous lipid deposition and explore differences between appropriate for gestational age (AGA), fetal growth restriction (FGR), and small for gestational age fetuses (SGAs). METHODS We prospectively recruited women with FGR and SGA-complicated pregnancies and retrospectively recruited the AGA cohort (sonographic estimated fetal weight [EFW] ≥ 10th centile). FGR was defined using the accepted Delphi criteria, and fetuses with an EFW < 10th centile that did not meet the Delphi criteria were defined as SGA. Fat-water and anatomical images were acquired in 3 T MRI scanners. The entire fetal subcutaneous fat was semi-automatically segmented. Three adiposity parameters were calculated: fat signal fraction (FSF) and two novel parameters, i.e., fat-to-body volume ratio (FBVR) and estimated total lipid content (ETLC = FSF*FBVR). Normal lipid deposition with gestation and differences between groups were assessed. RESULTS Thirty-seven AGA, 18 FGR, and 9 SGA pregnancies were included. All three adiposity parameters increased between 30 and 39 weeks (p < 0.001). All three adiposity parameters were significantly lower in FGR compared with AGA (p ≤ 0.001). Only ETLC and FSF were significantly lower in SGA compared with AGA using regression analysis (p = 0.018-0.036, respectively). Compared with SGA, FGR had a significantly lower FBVR (p = 0.011) with no significant differences in FSF and ETLC (p ≥ 0.053). CONCLUSIONS Whole-body subcutaneous lipid accretion increased throughout the third trimester. Reduced lipid deposition is predominant in FGR and may be used to differentiate FGR from SGA, assess FGR severity, and study other malnourishment pathologies. CLINICAL RELEVANCE STATEMENT Fetuses with growth restriction have reduced lipid deposition than appropriately developing fetuses measured using MRI. Reduced fat accretion is linked with worse outcomes and may be used for growth restriction risk stratification. KEY POINTS • Fat-water MRI can be used to assess the fetal nutritional status quantitatively. • Lipid deposition increased throughout the third trimester in AGA fetuses. • FGR and SGA have reduced lipid deposition compared with AGA fetuses, more predominant in FGR.
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Affiliation(s)
- Aviad Rabinowich
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Netanell Avisdris
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ayala Zilberman
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | | | - Sapir Lazar
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Jacky Herzlich
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Neonatal Intensive Care Unit, Dana Dwek Children's Hospital, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Bella Specktor-Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gustavo Malinger
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Liat Ben-Sira
- Department of Radiology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Liran Hiersch
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
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Rossi GMC, Mackowiak ALC, Açikgöz BC, Pierzchała K, Kober T, Hilbert T, Bastiaansen JAM. SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycled balanced SSFP signal profile. Magn Reson Med 2023; 90:2348-2361. [PMID: 37496187 DOI: 10.1002/mrm.29813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/19/2023] [Accepted: 07/12/2023] [Indexed: 07/28/2023]
Abstract
PURPOSE To develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase-cycled balanced SSFP (bSSFP) profile. METHODS SPARCQ uses phase-cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi-compartment dictionary matching. Each dictionary entry represents a single-compartment bSSFP profile with a specific off-resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off-resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding-artifact-free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water-fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1 H-MRS (phantoms) and with multiecho gradient-echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan-rescan experiments. RESULTS Simulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding-artifact-free quantitative maps and water-fat-separated images obtained with SPARCQ and ME-GRE demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512. CONCLUSION SPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.
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Affiliation(s)
- Giulia M C Rossi
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Adèle L C Mackowiak
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Berk Can Açikgöz
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Katarzyna Pierzchała
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jessica A M Bastiaansen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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Peng H, Cheng C, Wan Q, Liang D, Liu X, Zheng H, Zou C. Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat-water separation by field factor. Magn Reson Med 2023; 90:1830-1843. [PMID: 37379480 DOI: 10.1002/mrm.29774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/16/2023] [Accepted: 06/03/2023] [Indexed: 06/30/2023]
Abstract
PURPOSE To reduce the ambiguity between chemical shift and field inhomogeneity with flexible TE combinations by introducing a variable (field factor). THEORY AND METHODS The ambiguity between chemical shift and field inhomogeneity can be eliminated directly from the multiple in-phase images acquired at different TEs; however, it is only applicable to few echo combinations. In this study, we accommodated such an implementation in flexible TE combinations by introducing a new variable (field factor). The effects of the chemical shift were removed from the field inhomogeneity in the candidate solutions, thus reducing the ambiguity problem. To validate this concept, multi-echo MRI data acquired from various anatomies with different imaging parameters were tested. The derived fat and water images were compared with those of the state-of-the-art fat-water separation algorithms. RESULTS Robust fat-water separation was achieved with the accurate solution of field inhomogeneity, and no apparent fat-water swap was observed. In addition to the good performance, the proposed method is applicable to various fat-water separation applications, including different sequence types and flexible TE choices. CONCLUSION We propose an algorithm to reduce the ambiguity of chemical shift and field inhomogeneity and achieved robust fat-water separation in various applications.
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Affiliation(s)
- Hao Peng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuanli Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qian Wan
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chao Zou
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, Shenzhen, China
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Jordan VC, Sojoodi M, Shroff S, Pagan PG, Barrett SC, Wellen J, Tanabe KK, Chung RT, Caravan P, Gale EM. Molecular magnetic resonance imaging of liver inflammation using an oxidatively activated probe. JHEP Rep 2023; 5:100850. [PMID: 37818152 PMCID: PMC10561122 DOI: 10.1016/j.jhepr.2023.100850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 10/12/2023] Open
Abstract
Background & Aims Many liver diseases are driven by inflammation, but imaging to non-invasively diagnose and quantify liver inflammation has been underdeveloped. The inflammatory liver microenvironment is aberrantly oxidising owing in part to reactive oxygen species generated by myeloid leucocytes. We hypothesised that magnetic resonance imaging using the oxidatively activated probe Fe-PyC3A will provide a non-invasive biomarker of liver inflammation. Methods A mouse model of drug-induced liver injury was generated through intraperitoneal injection of a hepatoxic dose of acetaminophen. A mouse model of steatohepatitis was generated via a choline-deficient, l-amino acid defined high-fat diet (CDAHFD). Images were acquired dynamically before and after intravenous injection of Fe-PyC3A. The contrast agent gadoterate meglumine was used as a non-oxidatively activated negative control probe in mice fed CDAHFD. The (post-pre) Fe-PyC3A injection change in liver vs. muscle contrast-to-noise ratio (ΔCNR) recorded 2 min post-injection was correlated with liver function test values, histologic scoring assigned using the NASH Clinical Research Network criteria, and intrahepatic myeloid leucocyte composition determined by flow cytometry. Results For mice receiving i.p. injections of acetaminophen, intrahepatic neutrophil composition correlated poorly with liver test values but positively and significantly with ΔCNR (r = 0.64, p <0.0001). For mice fed CDAHFD, ΔCNR generated by Fe-PyC3A in the left lobe was significantly greater in mice meeting histologic criteria strongly associated with a diagnosis NASH compared to mice where histology was consistent with likely non-NASH (p = 0.0001), whereas no differential effect was observed using gadoterate meglumine. In mice fed CDAHFD, ΔCNR did not correlate strongly with fractional composition of any specific myeloid cell subpopulation as determined by flow cytometry. Conclusions Magnetic resonance imaging using Fe-PyC3A merits further evaluation as a non-invasive biomarker for liver inflammation. Impact and implications Non-invasive tests to diagnose and measure liver inflammation are underdeveloped. Inflammatory cells such as neutrophils release reactive oxygen species which creates an inflammatory liver microenvironment that can drive chemical oxidation. We recently invented a new class of magnetic resonance imaging probe that is made visible to the scanner only after chemical oxidation. Here, we demonstrate how this imaging technology could be applied as a non-invasive biomarker for liver inflammation.
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Affiliation(s)
- Veronica Clavijo Jordan
- Athinoula A. Martinos Center for Biomedical Imaging, The Institute for Innovation in Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mozhdeh Sojoodi
- Harvard Medical School, Boston, MA, USA
- Division of Gastrointestinal and Oncologic Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Stuti Shroff
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Patricia Gonzalez Pagan
- Athinoula A. Martinos Center for Biomedical Imaging, The Institute for Innovation in Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Stephen Cole Barrett
- Harvard Medical School, Boston, MA, USA
- Division of Gastrointestinal and Oncologic Surgery, Massachusetts General Hospital, Boston, MA, USA
| | | | - Kenneth K. Tanabe
- Harvard Medical School, Boston, MA, USA
- Division of Gastrointestinal and Oncologic Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - Raymond T. Chung
- Harvard Medical School, Boston, MA, USA
- Gastroenterology Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Peter Caravan
- Athinoula A. Martinos Center for Biomedical Imaging, The Institute for Innovation in Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Eric M. Gale
- Athinoula A. Martinos Center for Biomedical Imaging, The Institute for Innovation in Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Agarwal SM, Dissanayake J, Agid O, Bowie C, Brierley N, Chintoh A, De Luca V, Diaconescu A, Gerretsen P, Graff-Guerrero A, Hawco C, Herman Y, Hill S, Hum K, Husain MO, Kennedy JL, Kiang M, Kidd S, Kozloff N, Maslej M, Mueller DJ, Naeem F, Neufeld N, Remington G, Rotenberg M, Selby P, Siddiqui I, Szacun-Shimizu K, Tiwari AK, Thirunavukkarasu S, Wang W, Yu J, Zai CC, Zipursky R, Hahn M, Foussias G. Characterization and prediction of individual functional outcome trajectories in schizophrenia spectrum disorders (PREDICTS study): Study protocol. PLoS One 2023; 18:e0288354. [PMID: 37733693 PMCID: PMC10513234 DOI: 10.1371/journal.pone.0288354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/23/2023] [Indexed: 09/23/2023] Open
Abstract
Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual's functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
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Affiliation(s)
- Sri Mahavir Agarwal
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Banting and Best Diabetes Centre (BBDC), University of Toronto, Toronto, Canada
| | - Joel Dissanayake
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ofer Agid
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Christopher Bowie
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Noah Brierley
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Araba Chintoh
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Vincenzo De Luca
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Andreea Diaconescu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Philip Gerretsen
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ariel Graff-Guerrero
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Colin Hawco
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Yarissa Herman
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Sean Hill
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Kathryn Hum
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Muhammad Omair Husain
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - James L. Kennedy
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Michael Kiang
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Sean Kidd
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Nicole Kozloff
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Marta Maslej
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Daniel J. Mueller
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Farooq Naeem
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Nicholas Neufeld
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Gary Remington
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Martin Rotenberg
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Peter Selby
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Ishraq Siddiqui
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Kate Szacun-Shimizu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Arun K. Tiwari
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | | | - Wei Wang
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Joanna Yu
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Clement C. Zai
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Robert Zipursky
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
| | - Margaret Hahn
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Banting and Best Diabetes Centre (BBDC), University of Toronto, Toronto, Canada
| | - George Foussias
- Schizophrenia Division, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
- Temerty Faculty Institute of Medical Science, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
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Engelke K, Chaudry O, Gast L, Eldib MAB, Wang L, Laredo JD, Schett G, Nagel AM. Magnetic resonance imaging techniques for the quantitative analysis of skeletal muscle: State of the art. J Orthop Translat 2023; 42:57-72. [PMID: 37654433 PMCID: PMC10465967 DOI: 10.1016/j.jot.2023.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 09/02/2023] Open
Abstract
Background Magnetic resonance imaging (MRI) is the dominant 3D imaging modality to quantify muscle properties in skeletal muscle disorders, in inherited and acquired muscle diseases, and in sarcopenia, in cachexia and frailty. Methods This review covers T1 weighted and Dixon sequences, introduces T2 mapping, diffusion tensor imaging (DTI) and non-proton MRI. Technical concepts, strengths, limitations and translational aspects of these techniques are discussed in detail. Examples of clinical applications are outlined. For comparison 31P-and 13C-MR Spectroscopy are also addressed. Results MRI technology provides a rich toolset to assess muscle deterioration. In addition to classical measures such as muscle atrophy using T1 weighted imaging and fat infiltration using Dixon sequences, parameters characterizing inflammation from T2 maps, tissue sodium using non-proton MRI techniques or concentration or fiber architecture using diffusion tensor imaging may be useful for an even earlier diagnosis of the impairment of muscle quality. Conclusion Quantitative MRI provides new options for muscle research and clinical applications. Current limitations that also impair its more widespread use in clinical trials are lack of standardization, ambiguity of image segmentation and analysis approaches, a multitude of outcome parameters without a clear strategy which ones to use and the lack of normal data.
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Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
- Clario Inc, Germany
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Lena Gast
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | | | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Jean-Denis Laredo
- Service d’Imagerie Médicale, Institut Mutualiste Montsouris & B3OA, UMR CNRS 7052, Inserm U1271 Université de Paris-Cité, Paris, France
| | - Georg Schett
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Armin M. Nagel
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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Meneses JP, Arrieta C, Della Maggiora G, Besa C, Urbina J, Arrese M, Gana JC, Galgani JE, Tejos C, Uribe S. Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes. Eur Radiol 2023; 33:6557-6568. [PMID: 37014405 PMCID: PMC10415440 DOI: 10.1007/s00330-023-09576-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/09/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVE To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes. METHODS The proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth. RESULTS Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53). CONCLUSION MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times. CLINICAL RELEVANCE STATEMENT We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%. KEY POINTS • Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.
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Affiliation(s)
- Juan Pablo Meneses
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristobal Arrieta
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Faculty of Engineering, Universidad Alberto Hurtado, Santiago, Chile
| | | | - Cecilia Besa
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jesús Urbina
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Complejo Asistencial Dr. Sótero del Río, Santiago, Chile
| | - Marco Arrese
- Department of Gastroenterology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Juan Cristóbal Gana
- Department of Pediatric Gastroenterology and Nutrition, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Jose E Galgani
- Department of Health Sciences, Nutrition and Dietetics Career, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Nutrition, Diabetes and Metabolism, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Cristian Tejos
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Uribe
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
- Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile.
- Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile.
- Department of Medical Imaging and Radiation Sciences, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
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Velikina JV, Zhao R, Buelo CJ, Samsonov AA, Reeder SB, Hernando D. Data adaptive regularization with reference tissue constraints for liver quantitative susceptibility mapping. Magn Reson Med 2023; 90:385-399. [PMID: 36929781 PMCID: PMC11057046 DOI: 10.1002/mrm.29644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 02/24/2023] [Accepted: 03/05/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE To improve repeatability and reproducibility across acquisition parameters and reduce bias in quantitative susceptibility mapping (QSM) of the liver, through development of an optimized regularized reconstruction algorithm for abdominal QSM. METHODS An optimized approach to estimation of magnetic susceptibility distribution is formulated as a constrained reconstruction problem that incorporates estimates of the input data reliability and anatomical priors available from chemical shift-encoded imaging. The proposed data-adaptive method was evaluated with respect to bias, repeatability, and reproducibility in a patient population with a wide range of liver iron concentration (LIC). The proposed method was compared to the previously proposed and validated approach in liver QSM for two multi-echo spoiled gradient-recalled echo protocols with different acquisition parameters at 3T. Linear regression was used for evaluation of QSM methods against a reference FDA-approvedR 2 $$ {R}_2 $$ -based LIC measure andR 2 ∗ $$ {R}_2^{\ast } $$ measurements; repeatability/reproducibility were assessed by Bland-Altman analysis. RESULTS The data-adaptive method produced susceptibility maps with higher subjective quality due to reduced shading artifacts. For both acquisition protocols, higher linear correlation with bothR 2 $$ {R}_2 $$ - andR 2 ∗ $$ {R}_2^{\ast } $$ -based measurements were observed for the data-adaptive method (r 2 = 0 . 74 / 0 . 69 $$ {r}^2=0.74/0.69 $$ forR 2 $$ {R}_2 $$ ,0 . 97 / 0 . 95 $$ 0.97/0.95 $$ forR 2 ∗ $$ {R}_2^{\ast } $$ ) than the standard method (r 2 = 0 . 60 / 0 . 66 $$ {r}^2=0.60/0.66 $$ and0 . 79 / 0 . 88 $$ 0.79/0.88 $$ ). For both protocols, the data-adaptive method enabled better test-retest repeatability (repeatability coefficients 0.19/0.30 ppm for the data-adaptive method, 0.38/0.47 ppm for the standard method) and reproducibility across protocols (reproducibility coefficient 0.28 vs. 0.53ppm) than the standard method. CONCLUSIONS The proposed data-adaptive QSM algorithm may enable quantification of LIC with improved repeatability/reproducibility across different acquisition parameters as 3T.
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Affiliation(s)
- Julia V Velikina
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ruiyang Zhao
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Collin J Buelo
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
- Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
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Shamim AMKM, Panagiotopoulos N, Spahic A, Harris DT, Roldán-Alzate A, Wieben O, Reeder SB, Oechtering TH, Johnson KM. Fat mitigation strategies to improve image quality of radial 4D flow MRI in obese subjects. Magn Reson Med 2023; 90:444-457. [PMID: 37036023 PMCID: PMC10231668 DOI: 10.1002/mrm.29652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/10/2023] [Accepted: 03/14/2023] [Indexed: 04/11/2023]
Abstract
PURPOSE This study addresses the challenges in obtaining abdominal 4D flow MRI of obese patients. We aimed to evaluate spectral saturation and inner volume excitation as methods to mitigating artifacts originating from adipose signals, with the goal of enhancing image quality and improving quantification. METHODS Radial 4D flow MRI acquisitions with fat mitigation (inner volume excitation [IVE] and intermittent fat saturation [FS]) were compared to a standard slab selective excitation (SSE) in a test-retest study of 15 obese participants. IVE selectively excited a cylindrical region of interest, avoiding contamination from peripheral adipose tissue, while FS globally suppressed fat based on spectral selection. Acquisitions were evaluated qualitatively based on expert ratings and quantitatively based on conservation of mass, test-retest repeatability, and a divergence free quality metric. Errors were evaluated statistically using the absolute and relative errors, regression, and Bland-Altman analysis. RESULTS IVE demonstrated superior performance quantitatively in the conservation of mass analysis in the portal vein, with higher correlation and lower bias in regression analysis. IVE also produced flow fields with the lowest divergence error and was rated best in overall image quality, delineating small vessels, and producing the least streaking artifacts. Evaluation results did not differ significantly between FS and SSE. Test-retest reproducibility was similarly high for all sequences, with data suggesting biological variations dominate the technical variability. CONCLUSION IVE improved hemodynamic assessment of radial 4D flow MRI in the abdomen of obese participants while FS did not lead to significant improvements in image quality or flow metrics.
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Affiliation(s)
- A M K Muntasir Shamim
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, Wisconsin, USA
| | - Nikolaos Panagiotopoulos
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Universität zu Lübeck, Department of Radiology and Nuclear Medicine, Lübeck, Germany
| | - Alma Spahic
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - David T. Harris
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alejandro Roldán-Alzate
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Mechanical Engineering, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Oliver Wieben
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Scott B. Reeder
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Mechanical Engineering, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Thekla Helene Oechtering
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Universität zu Lübeck, Department of Radiology and Nuclear Medicine, Lübeck, Germany
| | - Kevin M. Johnson
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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Liu Y, Hamilton J, Jiang Y, Seiberlich N. Assessment of MRF for simultaneous T 1 and T 2 quantification and water-fat separation in the liver at 0.55 T. MAGMA (NEW YORK, N.Y.) 2023; 36:513-523. [PMID: 36574163 PMCID: PMC10293475 DOI: 10.1007/s10334-022-01057-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 11/10/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVE The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T. MATERIALS AND METHODS Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects. RESULTS In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s. CONCLUSION It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T1 and T2 quantification with no time penalty.
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Affiliation(s)
- Yuchi Liu
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA.
| | - Jesse Hamilton
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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Gaunt T, Sokolska M. Physiological assessment of the fetal body using MRI: current uses and potential directions. Br J Radiol 2023; 96:20221024. [PMID: 37310734 PMCID: PMC10321255 DOI: 10.1259/bjr.20221024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/14/2023] Open
Abstract
MRI assessment of the fetus using fast T2-weighted sequences is well established in defining alterations in fetal anatomy and structure, as a biomarker for disease, and in some cases prognostication. To date, the physiological assessment of the fetus using advanced sequences to characterise tissue perfusion and microarchitecture has largely been unused. Current methods of assessing fetal organ function are invasive and carry inherent risk. Therefore, the identification of imaging biomarkers of altered fetal physiology, and correlation with postnatal outcome, is attractive. This review describes the techniques which show promise for such a task and potential future directions.
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Affiliation(s)
- Trevor Gaunt
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Magdalena Sokolska
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals NHS Foundation Trust, London, United Kingdom
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44
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Dimov AV, Li J, Nguyen TD, Roberts AG, Spincemaille P, Straub S, Zun Z, Prince MR, Wang Y. QSM Throughout the Body. J Magn Reson Imaging 2023; 57:1621-1640. [PMID: 36748806 PMCID: PMC10192074 DOI: 10.1002/jmri.28624] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023] Open
Abstract
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Jiahao Li
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | | | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R. Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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Guglielmo FF, Barr RG, Yokoo T, Ferraioli G, Lee JT, Dillman JR, Horowitz JM, Jhaveri KS, Miller FH, Modi RY, Mojtahed A, Ohliger MA, Pirasteh A, Reeder SB, Shanbhogue K, Silva AC, Smith EN, Surabhi VR, Taouli B, Welle CL, Yeh BM, Venkatesh SK. Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists. Radiographics 2023; 43:e220181. [PMID: 37227944 DOI: 10.1148/rg.220181] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Flavius F Guglielmo
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Richard G Barr
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Takeshi Yokoo
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Giovanna Ferraioli
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - James T Lee
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Jonathan R Dillman
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Jeanne M Horowitz
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Kartik S Jhaveri
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Frank H Miller
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Roshan Y Modi
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Amirkasra Mojtahed
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Michael A Ohliger
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Ali Pirasteh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Scott B Reeder
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Krishna Shanbhogue
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Alvin C Silva
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Elainea N Smith
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Venkateswar R Surabhi
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Bachir Taouli
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Christopher L Welle
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Benjamin M Yeh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Sudhakar K Venkatesh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
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Lee A, Choi YJ, Jeon KJ, Han SS, Lee C. Development and accuracy validation of a fat fraction imaging biomarker for sialadenitis in the parotid gland. BMC Oral Health 2023; 23:347. [PMID: 37264360 DOI: 10.1186/s12903-023-03024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 05/08/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging. This study aimed to establish a diagnostic reference for sialadenitis based on the quantitative magnetic resonance imaging (MRI) biomarker IDEAL-IQ and assess its accuracy. METHODS Patients with sialadenitis (n = 46) and control subjects (n = 90) that underwent MRI were selected. Considering that the IDEAL-IQ value is a sensitive fat fractional marker to the body mass index (BMI), all subjects were also categorized as under-, normal-, and overweight. The fat fraction of parotid gland in the control and sialadenitis groups were obtained using IDEAL-IQ map. The values from the subjects in the control and sialadenitis groups were compared in each BMI category. For comparison, t-tests and receiver operating characteristic (ROC) curve analyses were performed. RESULTS The IDEAL-IQ fat faction of the control and sialadenitis glands were 38.57% and 23.69%, respectively, and the differences were significant. The values were significantly lower in the sialadenitis group (P), regardless of the BMI types. The area under the ROC curve (AUC) was 0.83 (cut-off value: 28.72) in patients with sialadenitis. The AUC for under-, normal-, and overweight individuals were 0.78, 0.81, and 0.92, respectively. CONCLUSIONS The fat fraction marker based on the IDEAL-IQ method was useful as an objective indicator for diagnosing sialadenitis. This marker would aid less-experienced clinicians in diagnosing sialadenitis.
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Affiliation(s)
- Ari Lee
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Korea
| | - Yoon Joo Choi
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Korea
| | - Kug Jin Jeon
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Korea
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, College of Dentistry, Yonsei University, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Korea.
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47
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Silva J, Milovic C, Lambert M, Montalba C, Arrieta C, Irarrazaval P, Uribe S, Tejos C. Toward a realistic in silico abdominal phantom for QSM. Magn Reson Med 2023; 89:2402-2418. [PMID: 36695213 PMCID: PMC10952412 DOI: 10.1002/mrm.29597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/18/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023]
Abstract
PURPOSE QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms. METHODS Synthetic susceptibility andR 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility andR 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM andR 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms. RESULTS QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms. CONCLUSION The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility,R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.
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Affiliation(s)
- Javier Silva
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Carlos Milovic
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- School of Electrical EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile
| | - Mathias Lambert
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Cristian Montalba
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristóbal Arrieta
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
| | - Pablo Irarrazaval
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de ChileSantiagoChile
| | - Sergio Uribe
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
- Department of Radiology, School of MedicinePontificia Universidad Católica de ChileSantiagoChile
| | - Cristian Tejos
- Department of Electrical EngineeringPontificia Universidad Católica de Chile
SantiagoChile
- Biomedical Imaging CenterPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH)SantiagoChile
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48
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Guenthner C, Peereboom SM, Dillinger H, McGrath C, Albannay MM, Vishnevskiy V, Fuetterer M, Luechinger R, Jenneskens T, Sturzenegger U, Overweg J, Koken P, Börnert P, Kozerke S. Ramping down a clinical 3 T scanner: a journey into MRI and MRS at 0.75 T. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01089-9. [PMID: 37171689 PMCID: PMC10386956 DOI: 10.1007/s10334-023-01089-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/06/2023] [Accepted: 04/01/2023] [Indexed: 05/13/2023]
Abstract
OBJECT Lower-field MR is reemerging as a viable, potentially cost-effective alternative to high-field MR, thanks to advances in hardware, sequence design, and reconstruction over the past decades. Evaluation of lower field strengths, however, is limited by the availability of lower-field systems on the market and their considerable procurement costs. In this work, we demonstrate a low-cost, temporary alternative to purchasing a dedicated lower-field MR system. MATERIALS AND METHODS By ramping down an existing clinical 3 T MRI system to 0.75 T, proton signals can be acquired using repurposed 13C transmit/receive hardware and the multi-nuclei spectrometer interface. We describe the ramp-down procedure and necessary software and hardware changes to the system. RESULTS Apart from presenting system characterization results, we show in vivo examples of cardiac cine imaging, abdominal two- and three-point Dixon-type water/fat separation, water/fat-separated MR Fingerprinting, and point-resolved spectroscopy. In addition, the ramp-down approach allows unique comparisons of, e.g., gradient fidelity of the same MR system operated at different field strengths using the same receive chain, gradient coils, and amplifiers. DISCUSSION Ramping down an existing MR system may be seen as a viable alternative for lower-field MR research in groups that already own multi-nuclei hardware and can also serve as a testing platform for custom-made multi-nuclei transmit/receive coils.
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Affiliation(s)
- Christian Guenthner
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
| | | | - Hannes Dillinger
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Charles McGrath
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | | | - Valery Vishnevskiy
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Max Fuetterer
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Roger Luechinger
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | | | | | | | | | | | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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49
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Tan Z, Unterberg-Buchwald C, Blumenthal M, Scholand N, Schaten P, Holme C, Wang X, Raddatz D, Uecker M. Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1374-1387. [PMID: 37015368 PMCID: PMC10368089 DOI: 10.1109/tmi.2022.3228075] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.
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50
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Triay Bagur A, McClymont D, Hutton C, Borghetto A, Gyngell ML, Aljabar P, Robson MD, Brady M, Bulte DP. Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation. Magn Reson Imaging 2023; 97:102-111. [PMID: 36632946 DOI: 10.1016/j.mri.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/06/2023] [Indexed: 01/11/2023]
Abstract
Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.
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Affiliation(s)
- Alexandre Triay Bagur
- Department of Engineering Science, University of Oxford, Oxford, UK; Perspectum Ltd, Oxford, UK.
| | | | | | | | | | | | | | | | - Daniel P Bulte
- Department of Engineering Science, University of Oxford, Oxford, UK
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