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Tripp DP, Kunze KP, Crabb MG, Prieto C, Neji R, Botnar RM. Simultaneous 3D T 1 $$ {\mathrm{T}}_1 $$ , T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction mapping with respiratory-motion correction for comprehensive liver tissue characterization at 0.55 T. Magn Reson Med 2024; 92:2433-2446. [PMID: 39075868 DOI: 10.1002/mrm.30236] [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: 12/01/2023] [Revised: 06/03/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024]
Abstract
PURPOSE To develop a framework for simultaneous three-dimensional (3D) mapping ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction in the liver at 0.55 T. METHODS The proposed sequence acquires four interleaved 3D volumes with a two-echo Dixon readout.T 1 $$ {\mathrm{T}}_1 $$ andT 2 $$ {\mathrm{T}}_2 $$ are encoded into each volume via preparation modules, and dictionary matching allows simultaneous estimation ofT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , andM 0 $$ {M}_0 $$ for water and fat separately. 2D image navigators permit respiratory binning, and motion fields from nonrigid registration between bins are used in a nonrigid respiratory-motion-corrected reconstruction, enabling 100% scan efficiency from a free-breathing acquisition. The integrated nature of the framework ensures the resulting maps are always co-registered. RESULTS T 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction measurements in phantoms correlated strongly (adjustedr 2 > 0 . 98 $$ {r}^2>0.98 $$ ) with reference measurements. Mean liver tissue parameter values in 10 healthy volunteers were427 ± 22 $$ 427\pm 22 $$ ,47 . 7 ± 3 . 3 ms $$ 47.7\pm 3.3\;\mathrm{ms} $$ , and7 ± 2 % $$ 7\pm 2\% $$ forT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat signal fraction, giving biases of71 $$ 71 $$ ,- 30 . 0 ms $$ -30.0\;\mathrm{ms} $$ , and- 5 $$ -5 $$ percentage points, respectively, when compared to conventional methods. CONCLUSION A novel sequence for comprehensive characterization of liver tissue at 0.55 T was developed. The sequence provides co-registered 3DT 1 $$ {\mathrm{T}}_1 $$ ,T 2 $$ {\mathrm{T}}_2 $$ , and fat-signal-fraction maps with full coverage of the liver, from a single nine-and-a-half-minute free-breathing scan. Further development is needed to achieve accurate proton-density fat fraction (PDFF) estimation in vivo.
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Affiliation(s)
- Donovan P Tripp
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Karl P Kunze
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Michael G Crabb
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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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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Cao T, Hu Z, Mao X, Chen Z, Kwan AC, Xie Y, Berman DS, Li D, Christodoulou AG. Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking. Magn Reson Med 2024; 92:1421-1439. [PMID: 38726884 PMCID: PMC11262969 DOI: 10.1002/mrm.30131] [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: 09/14/2023] [Revised: 03/20/2024] [Accepted: 04/08/2024] [Indexed: 05/15/2024]
Abstract
PURPOSE To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Zheyuan Hu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Xianglun Mao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Alan C. Kwan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Departments of Imaging and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S. Berman
- Departments of Imaging and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
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Manhard MK, Kilpattu Ramaniharan A, Tkach JA, Trout AT, Dillman JR, Pednekar AS. Simultaneous Multiparameter Mapping of the Liver in a Single Breath-Hold or Respiratory-Triggered Acquisition Using Multi-Inversion Spin and Gradient Echo MRI. J Magn Reson Imaging 2024. [PMID: 39192381 DOI: 10.1002/jmri.29584] [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: 04/25/2024] [Revised: 08/06/2024] [Accepted: 08/07/2024] [Indexed: 08/29/2024] Open
Abstract
BACKGROUND Quantitative parametric mapping is an increasingly important tool for noninvasive assessment of chronic liver disease. Conventional parametric mapping techniques require multiple breath-held acquisitions and provide limited anatomic coverage. PURPOSE To investigate a multi-inversion spin and gradient echo (MI-SAGE) technique for simultaneous estimation of T1, T2, and T2* of the liver. STUDY TYPE Prospective. SUBJECTS Sixteen research participants, both adult and pediatric (age 17.5 ± 4.6 years, eight male), with and without known liver disease (seven asymptomatic healthy controls, two fibrotic liver disease, five steatotic liver disease, and two fibrotic and steatotic liver disease). FIELD STRENGTH/SEQUENCE 1.5 T, single breath-hold and respiratory triggered MI-SAGE, breath-hold modified Look-Locker inversion recovery (MOLLI, T1 mapping), breath-hold gradient and spin echo (GRASE, T2 mapping), and multiple gradient echo (mGRE, T2* mapping) sequences. ASSESSMENT Agreement between hepatic T1, T2, and T2* estimated using MI-SAGE and conventional parametric mapping sequences was evaluated. Repeatability and reproducibility of MI-SAGE were evaluated using a same-session acquisition and second-session acquisition. STATISTICAL TESTS Bland-Altman analysis with bias assessment and limits of agreement (LOA) and intraclass correlation coefficients (ICC). RESULTS Hepatic T1, T2, and T2* estimates obtained using the MI-SAGE technique had mean biases of 72 (LOA: -22 to 166) msec, -3 (LOA: -10 to 5) msec, and 2 (LOA: -5 to 8) msec (single breath-hold) and 36 (LOA: -43 to 120) msec, -3 (LOA: -17 to 11) msec, and 4 (LOA: -3 to 11) msec (respiratory triggered), respectively, in comparison to conventional acquisitions using MOLLI, GRASE, and mGRE. All MI-SAGE estimates had strong repeatability and reproducibility (ICC > 0.72). DATA CONCLUSION Hepatic T1, T2, and T2* estimates obtained using an MI-SAGE technique were comparable to conventional methods, although there was a 12%/6% for breath-hold/respiratory triggered underestimation of T1 values compared to MOLLI. Both respiratory triggered and breath-hold MI-SAGE parameter maps demonstrated strong repeatability and reproducibility. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Mary Kate Manhard
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Amol S Pednekar
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
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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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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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Guan X, Yang HJ, Zhang X, Wang N, Han H, Tang R, Hu Z, Youssef K, Vora K, Krishnam MS, Christodoulou AG, Li D, Sharif B, Dharmakumar R. Non-electrocardiogram-gated, free-breathing, off-resonance reduced, high-resolution, whole-heart myocardial T 2 * mapping at 3 T within 5 min. Magn Reson Med 2024; 91:1936-1950. [PMID: 38174593 DOI: 10.1002/mrm.29968] [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: 04/12/2023] [Revised: 11/21/2023] [Accepted: 11/26/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE Widely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated. METHODS A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol. RESULTS Spatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias. CONCLUSION The proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.
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Affiliation(s)
- Xingmin Guan
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Hsin-Jung Yang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xinheng Zhang
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Richard Tang
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Zhehao Hu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Khalid Youssef
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Keyur Vora
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Mayil S Krishnam
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Anthony G Christodoulou
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Behzad Sharif
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
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8
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Li S, Wang Z, Ding Z, She H, Du YP. Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling. Quant Imaging Med Surg 2024; 14:2884-2903. [PMID: 38617145 PMCID: PMC11007520 DOI: 10.21037/qims-23-1396] [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: 10/06/2023] [Accepted: 02/13/2024] [Indexed: 04/16/2024]
Abstract
Background Multi-echo chemical-shift-encoded magnetic resonance imaging (MRI) has been widely used for fat quantification and fat suppression in clinical liver examinations. Clinical liver water-fat imaging typically requires breath-hold acquisitions, with the free-breathing acquisition method being more desirable for patient comfort. However, the acquisition for free-breathing imaging could take up to several minutes. The purpose of this study is to accelerate four-dimensional free-breathing whole-liver water-fat MRI by jointly using high-dimensional deep dictionary learning and model-guided (MG) reconstruction. Methods A high-dimensional model-guided deep dictionary learning (HMDDL) algorithm is proposed for the acceleration. The HMDDL combines the powers of the high-dimensional dictionary learning neural network (hdDLNN) and the chemical shift model. The neural network utilizes the prior information of the dynamic multi-echo data in spatial respiratory motion, and echo dimensions to exploit the features of images. The chemical shift model is used to guide the reconstruction of field maps, R 2 ∗ maps, water images, and fat images. Data acquired from ten healthy subjects and ten subjects with clinically diagnosed nonalcoholic fatty liver disease (NAFLD) were selected for training. Data acquired from one healthy subject and two NAFLD subjects were selected for validation. Data acquired from five healthy subjects and five NAFLD subjects were selected for testing. A three-dimensional (3D) blipped golden-angle stack-of-stars multi-gradient-echo pulse sequence was designed to accelerate the data acquisition. The retrospectively undersampled data were used for training, and the prospectively undersampled data were used for testing. The performance of the HMDDL was evaluated in comparison with the compressed sensing-based water-fat separation (CS-WF) algorithm and a parallel non-Cartesian recurrent neural network (PNCRNN) algorithm. Results Four-dimensional water-fat images with ten motion states for whole-liver are demonstrated at several R values. In comparison with the CS-WF and PNCRNN, the HMDDL improved the mean peak signal-to-noise ratio (PSNR) of images by 9.93 and 2.20 dB, respectively, and improved the mean structure similarity (SSIM) of images by 0.058 and 0.009, respectively, at R=10. The paired t-test shows that there was no significant difference between HMDDL and ground truth for proton-density fat fraction (PDFF) and R 2 ∗ values at R up to 10. Conclusions The proposed HMDDL enables features of water images and fat images from the highly undersampled multi-echo data along spatial, respiratory motion, and echo dimensions, to improve the performance of accelerated four-dimensional (4D) free-breathing water-fat imaging.
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Affiliation(s)
- Shuo Li
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhijun Wang
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zekang Ding
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiping P Du
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Christodoulou AG, Cruz G, Arami A, Weingärtner S, Artico J, Peters D, Seiberlich N. The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1). J Cardiovasc Magn Reson 2024; 26:100997. [PMID: 38237900 PMCID: PMC11211239 DOI: 10.1016/j.jocmr.2024.100997] [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: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Gastao Cruz
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Ayda Arami
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | | | - Dana Peters
- Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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10
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Mao A, Flassbeck S, Gultekin C, Assländer J. Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI. ARXIV 2023:arXiv:2305.00326v2. [PMID: 37961734 PMCID: PMC10635289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.
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Affiliation(s)
- Andrew Mao
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
| | | | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Jakob Assländer
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
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Hernando D, van der Heijden RA, Reeder SB. A better understanding of liver T1. Eur Radiol 2023; 33:6841-6843. [PMID: 37552263 DOI: 10.1007/s00330-023-10067-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 08/09/2023]
Affiliation(s)
- Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Rianne A van der Heijden
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, USA
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12
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Geng R, Buelo CJ, Sundaresan M, Starekova J, Panagiotopoulos N, Oechtering TH, Lawrence EM, Ignaciuk M, Reeder SB, Hernando D. Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation. J Magn Reson Imaging 2023; 58:429-441. [PMID: 36583550 PMCID: PMC10310884 DOI: 10.1002/jmri.28564] [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: 09/06/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND There is an unmet need for fully automated image prescription of the liver to enable efficient, reproducible MRI. PURPOSE To develop and evaluate artificial intelligence (AI)-based liver image prescription. STUDY TYPE Prospective. POPULATION A total of 570 female/469 male patients (age: 56 ± 17 years) with 72%/8%/20% assigned randomly for training/validation/testing; two female/four male healthy volunteers (age: 31 ± 6 years). FIELD STRENGTH/SEQUENCE 1.5 T, 3.0 T; spin echo, gradient echo, bSSFP. ASSESSMENT A total of 1039 three-plane localizer acquisitions (26,929 slices) from consecutive clinical liver MRI examinations were retrieved retrospectively and annotated by six radiologists. The localizer images and manual annotations were used to train an object-detection convolutional neural network (YOLOv3) to detect multiple object classes (liver, torso, and arms) across localizer image orientations and to output corresponding 2D bounding boxes. Whole-liver image prescription in standard orientations was obtained based on these bounding boxes. 2D detection performance was evaluated on test datasets by calculating intersection over union (IoU) between manual and automated labeling. 3D prescription accuracy was calculated by measuring the boundary mismatch in each dimension and percentage of manual volume covered by AI prescription. The automated prescription was implemented on a 3 T MR system and evaluated prospectively on healthy volunteers. STATISTICAL TESTS Paired t-tests (threshold = 0.05) were conducted to evaluate significance of performance difference between trained networks. RESULTS In 208 testing datasets, the proposed method with full network had excellent agreement with manual annotations, with median IoU > 0.91 (interquartile range < 0.09) across all seven classes. The automated 3D prescription was accurate, with shifts <2.3 cm in superior/inferior dimension for 3D axial prescription for 99.5% of test datasets, comparable to radiologists' interreader reproducibility. The full network had significantly superior performance than the tiny network for 3D axial prescription in patients. Automated prescription performed well across single-shot fast spin-echo, gradient-echo, and balanced steady-state free-precession sequences in the prospective study. DATA CONCLUSION AI-based automated liver image prescription demonstrated promising performance across the patients, pathologies, and field strengths studied. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Ruiqi Geng
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Collin J. Buelo
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Mahalakshmi Sundaresan
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Jitka Starekova
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Nikolaos Panagiotopoulos
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany (Haus A, Ratzeburger Allee 160, 23562 Lübeck, Germany)
| | - Thekla H. Oechtering
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany (Haus A, Ratzeburger Allee 160, 23562 Lübeck, Germany)
| | - Edward M. Lawrence
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Marcin Ignaciuk
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Scott B. Reeder
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medicine, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Emergency Medicine, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA (1111 Highland Ave, Madison, WI 53705)
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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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14
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Maatman IT, Ypma S, Kachelrieß M, Berker Y, van der Bijl E, Block KT, Hermans JJ, Maas MC, Scheenen TWJ. Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging. Magn Reson Med 2023; 89:1931-1944. [PMID: 36594436 DOI: 10.1002/mrm.29576] [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: 09/21/2022] [Revised: 11/23/2022] [Accepted: 12/19/2022] [Indexed: 01/04/2023]
Abstract
PURPOSE To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes. METHODS Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies. RESULTS The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required. CONCLUSION SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.
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Affiliation(s)
- Ivo T Maatman
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sjoerd Ypma
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marc Kachelrieß
- Division of X-Ray Imaging and Computed Tomography, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yannick Berker
- Hopp Children's Cancer Center Heidelberg (KiTZ), Heidelberg, Germany.,Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.,National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - Erik van der Bijl
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Kai Tobias Block
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - John J Hermans
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marnix C Maas
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tom W J Scheenen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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15
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Ringe KI, Yoon JH. Strategies and Techniques for Liver Magnetic Resonance Imaging: New and Pending Applications for Routine Clinical Practice. Korean J Radiol 2023; 24:180-189. [PMID: 36788770 PMCID: PMC9971842 DOI: 10.3348/kjr.2022.0838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/11/2022] [Accepted: 12/22/2022] [Indexed: 02/16/2023] Open
Affiliation(s)
- Kristina I. Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
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16
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Wang Y, Qi H, Wang Y, Xiao M, Xiang C, Dong J, Chen H. Free-breathing simultaneous water-fat separation and T1 mapping of the whole liver (SWALI) with isotropic resolution using 3D golden-angle radial trajectory. Quant Imaging Med Surg 2023; 13:912-923. [PMID: 36819282 PMCID: PMC9929423 DOI: 10.21037/qims-22-748] [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: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023]
Abstract
Background Conventional liver T1 mapping techniques are typically performed under breath-holding conditions; they have limited slice coverage and often rely on multiple acquisitions. Furthermore, liver fat affects the accuracy of T1 quantification. Therefore, we aim to propose a free-breathing technique for simultaneous water-fat separation and T1 mapping of the whole liver (SWALI) in a single scan. Methods The proposed SWALI sequence included an inversion recovery (IR) preparation pulse followed by a series of multiecho three-dimensional (3D) golden-angle radial acquisitions. For each echo time (TE), a series of images containing a mix of water and fat were reconstructed using a sliding window method. For each inversion time (TI), water and fat were separated, and then water and fat T1 estimation was conducted. The fat fraction (FF) was calculated based on the last TI image. The FF and water T1 quantification accuracy were compared with the gold standard sequences in the phantom. The in vivo feasibility was tested in 9 healthy volunteers, 2 patients with fatty liver, and 3 patients with hepatocellular carcinoma (HCC). The reproducibility was evaluated in the patients with fatty liver and in the healthy volunteers. Results The mean FF and the mean water T1 values obtained by the SWALI sequence showed good agreements with chemical shift-encoded magnetic resonance imaging (CSE-MRI; r=0.998; P<0.001) and fat-suppressed (FS) IR-spin echo (SE; r=0.997; P<0.001) in the phantom. For the patients with fatty liver and the healthy volunteers, the SWALI sequence showed no significant difference with CSE-MRI in FF quantification (P=0.53). In T1 quantification, comparable T1 values were obtained with the SWALI sequence and modified Look-Locker inversion recovery (MOLLI; P=0.10) in healthy volunteers, while the water T1 estimated by the SWALI sequence was significantly lower than the water-fat compound T1 estimated by MOLLI (P<0.001) in patients with fatty liver. In the reproducibility study, the intraclass correlation coefficients (ICCs) for the estimated FF and water T1 were 0.997 and 0.943, respectively. Water T1 of the patients with HCC calculated using the SWALI sequence showed a significant reduction after the contrast administration (P<0.001). Conclusions Free-breathing water-fat separation and T1 mapping of the whole liver with 2.5 mm isotropic spatial resolution were achieved simultaneously using the SWALI sequence in a 5-min scan.
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Affiliation(s)
- Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | | | - Ming Xiao
- Department of Hepatobiliary Surgery, The Second Hospital of Shandong University, Jinan, China;,Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Canhong Xiang
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Jiahong Dong
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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17
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Free-breathing and instantaneous abdominal T 2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction. Eur Radiol 2023:10.1007/s00330-023-09417-2. [PMID: 36692597 DOI: 10.1007/s00330-023-09417-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/12/2022] [Accepted: 01/01/2023] [Indexed: 01/25/2023]
Abstract
OBJECTIVES To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.
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Fotaki A, Velasco C, Prieto C, Botnar RM. Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches. Front Cardiovasc Med 2023; 9:991383. [PMID: 36756640 PMCID: PMC9899858 DOI: 10.3389/fcvm.2022.991383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T1, T1ρ, T2 and T2* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.
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Affiliation(s)
- Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,*Correspondence: Anastasia Fotaki,
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile,Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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19
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Roberts NT, Tamada D, Muslu Y, Hernando D, Reeder SB. Confounder-corrected T 1 mapping in the liver through simultaneous estimation of T 1 , PDFF, R 2 * , and B 1 + in a single breath-hold acquisition. Magn Reson Med 2023; 89:2186-2203. [PMID: 36656152 PMCID: PMC10139739 DOI: 10.1002/mrm.29590] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 12/23/2022] [Accepted: 01/01/2023] [Indexed: 01/20/2023]
Abstract
PURPOSE Quantitative volumetric T1 mapping in the liver has the potential to aid in the detection, diagnosis, and quantification of liver fibrosis, inflammation, and spatially resolved liver function. However, accurate measurement of hepatic T1 is confounded by the presence of fat and inhomogeneous B 1 + $$ {B}_1^{+} $$ excitation. Furthermore, scan time constraints related to respiratory motion require tradeoffs of reduced volumetric coverage and/or increased acquisition time. This work presents a novel 3D acquisition and estimation method for confounder-corrected T1 measurement over the entire liver within a single breath-hold through simultaneous estimation of T1 , fat and B 1 + $$ {B}_1^{+} $$ . THEORY AND METHODS The proposed method combines chemical shift encoded MRI and variable flip angle MRI with a B 1 + $$ {B}_1^{+} $$ mapping technique to enable confounder-corrected T1 mapping. The method was evaluated theoretically and demonstrated in both phantom and in vivo acquisitions at 1.5 and 3.0T. At 1.5T, the method was evaluated both pre- and post- contrast enhancement in healthy volunteers. RESULTS The proposed method demonstrated excellent linear agreement with reference inversion-recovery spin-echo based T1 in phantom acquisitions at both 1.5 and 3.0T, with minimal bias (5.2 and 45 ms, respectively) over T1 ranging from 200-1200 ms. In vivo results were in general agreement with reference saturation-recovery based 2D T1 maps (SMART1 Map, GE Healthcare). CONCLUSION The proposed 3D T1 mapping method accounts for fat and B 1 + $$ {B}_1^{+} $$ confounders through simultaneous estimation of T1 , B 1 + $$ {B}_1^{+} $$ , PDFF and R 2 * $$ {R}_2^{\ast } $$ . It demonstrates strong linear agreement with reference T1 measurements, with low bias and high precision, and can achieve full liver coverage in a single breath-hold.
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Affiliation(s)
- Nathan T Roberts
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Daiki Tamada
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Yavuz Muslu
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Diego Hernando
- 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 Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA.,Department of Biomedical Engineering, 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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Cao T, Wang N, Kwan AC, Lee HL, Mao X, Xie Y, Nguyen KL, Colbert CM, Han F, Han P, Han H, Christodoulou AG, Li D. Free-breathing, non-ECG, simultaneous myocardial T 1 , T 2 , T 2 *, and fat-fraction mapping with motion-resolved cardiovascular MR multitasking. Magn Reson Med 2022; 88:1748-1763. [PMID: 35713184 PMCID: PMC9339519 DOI: 10.1002/mrm.29351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/18/2022] [Accepted: 05/17/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop a free-breathing, non-electrocardiogram technique for simultaneous myocardial T1 , T2 , T2 *, and fat-fraction (FF) mapping in a single scan. METHODS The MR Multitasking framework is adapted to quantify T1 , T2 , T2 *, and FF simultaneously. A variable TR scheme is developed to preserve temporal resolution and imaging efficiency. The underlying high-dimensional image is modeled as a low-rank tensor, which allows accelerated acquisition and efficient reconstruction. The accuracy and/or repeatability of the technique were evaluated on static and motion phantoms, 12 healthy volunteers, and 3 patients by comparing to the reference techniques. RESULTS In static and motion phantoms, T1 /T2 /T2 */FF measurements showed substantial consistency (R > 0.98) and excellent agreement (intraclass correlation coefficient > 0.93) with reference measurements. In human subjects, the proposed technique yielded repeatable T1 , T2 , T2 *, and FF measurements that agreed with those from references. CONCLUSIONS The proposed free-breathing, non-electrocardiogram, motion-resolved Multitasking technique allows simultaneous quantification of myocardial T1 , T2 , T2 *, and FF in a single 2.5-min scan.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Nan Wang
- Radiology Department, Stanford University, Stanford, California, USA
| | - Alan C. Kwan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Imaging and Cardiology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xianglun Mao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Kim-Lien Nguyen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
- David Geffen School of Medicine and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Caroline M. Colbert
- David Geffen School of Medicine and VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
- Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Pei Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA
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Li S, Shen C, Ding Z, She H, Du YP. Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning. Magn Reson Med 2022; 88:1851-1866. [PMID: 35649172 DOI: 10.1002/mrm.29307] [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: 03/17/2022] [Revised: 04/30/2022] [Accepted: 05/02/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data. METHODS A model-guided deep learning water-fat separation framework is proposed for the acceleration using Cartesian/radial undersampling data. The proposed MGDL-WF combines the power of CSE water-fat imaging model and data-driven deep learning by jointly using a multi-peak fat model and a modified residual U-net network. The model is used to guide the image reconstruction, and the network is used to capture the artifacts induced by the undersampling. A data consistency layer is used in MGDL-WF to ensure the output images to be consistent with the k-space measurements. A Gauss-Newton iteration algorithm is adapted for the gradient updating of the networks. RESULTS Compared with the compressed sensing water-fat separation (CS-WF) algorithm/2-step procedure algorithm, the MGDL-WF increased peak signal-to-noise ratio (PSNR) by 5.31/5.23, 6.11/4.54, and 4.75 dB/1.88 dB with Cartesian sampling, and by 4.13/6.53, 2.90/4.68, and 1.68 dB/3.48 dB with radial sampling, at acceleration rates (R) of 4, 6, and 8, respectively. By using MGDL-WF, radial sampling increased the PSNR by 2.07 dB at R = 8, compared with Cartesian sampling. CONCLUSIONS The proposed MGDL-WF enables exploiting features of the water images and fat images from the undersampled multi-echo data, leading to improved performance in the accelerated CSE water-fat imaging. By using MGDL-WF, radial sampling can further improve the image quality with comparable scan time in comparison with Cartesian sampling.
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Affiliation(s)
- Shuo Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chenfei Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zekang Ding
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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22
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Feng L. Golden-Angle Radial MRI: Basics, Advances, and Applications. J Magn Reson Imaging 2022; 56:45-62. [PMID: 35396897 PMCID: PMC9189059 DOI: 10.1002/jmri.28187] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 12/21/2022] Open
Abstract
In recent years, golden‐angle radial sampling has received substantial attention and interest in the magnetic resonance imaging (MRI) community, and it has become a popular sampling trajectory for both research and clinical use. However, although the number of relevant techniques and publications has grown rapidly, there is still a lack of a review paper that provides a comprehensive overview and summary of the basics of golden‐angle rotation, the advantages and challenges/limitations of golden‐angle radial sampling, and recommendations in using different types of golden‐angle radial trajectories for MRI applications. Such a review paper is expected to be helpful both for clinicians who are interested in learning the potential benefits of golden‐angle radial sampling and for MRI physicists who are interested in exploring this research direction. The main purpose of this review paper is thus to present an overview and summary about golden‐angle radial MRI sampling. The review consists of three sections. The first section aims to answer basic questions such as: what is a golden angle; how is the golden angle calculated; why is golden‐angle radial sampling useful, and what are its limitations. The second section aims to review more advanced trajectories of golden‐angle radial sampling, including tiny golden‐angle rotation, stack‐of‐stars golden‐angle radial sampling, and three‐dimensional (3D) kooshball golden‐angle radial sampling. Their respective advantages and limitations and potential solutions to address these limitations are also discussed. Finally, the third section reviews MRI applications that can benefit from golden‐angle radial sampling and provides recommendations to readers who are interested in implementing golden‐angle radial trajectories in their MRI studies.
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Affiliation(s)
- Li Feng
- BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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