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Li X, Kim J, Yang M, Ok AH, Zbýň Š, Link TM, Majumdar S, Ma CB, Spindler KP, Winalski CS. Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades. Skeletal Radiol 2024; 53:1761-1781. [PMID: 38980364 PMCID: PMC11303573 DOI: 10.1007/s00256-024-04734-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 07/10/2024]
Abstract
Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.
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Affiliation(s)
- Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA.
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA.
| | - Jeehun Kim
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmet H Ok
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Štefan Zbýň
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - Sharmilar Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco (UCSF), San Francisco, CA, USA
| | - C Benjamin Ma
- Department of Orthopaedic Surgery, UCSF, San Francisco, CA, USA
| | - Kurt P Spindler
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Carl S Winalski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, 9500 Euclid Avenue, ND20, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
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Zhang HZ, Elsaid NMH, Sun H, Tagare HD, Galiana G. Efficient standardization of clinical T 2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent. Magn Reson Med 2024. [PMID: 38988054 DOI: 10.1002/mrm.30214] [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: 03/07/2024] [Revised: 06/05/2024] [Accepted: 06/21/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To standardize T 2 $$ {}_2 $$ -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T 2 $$ {}_2 $$ maps with the goal of removing scanner- and/or protocol-specific heterogeneity. METHODS The T 2 $$ {}_2 $$ map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). RESULTS The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T 2 $$ {}_2 $$ mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T 2 $$ {}_2 $$ maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T 2 $$ {}_2 $$ w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. CONCLUSION The novel methodological development creates an efficient algorithm that allows for T 2 $$ {}_2 $$ map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T 2 $$ {}_2 $$ maps from TSE data with typical clinical parameters has not been previously reported.
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Affiliation(s)
- Horace Z Zhang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Nahla M H Elsaid
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
| | - Heng Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Hemant D Tagare
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
- Department of Electrical Engineering, Yale University, New Haven, Connecticut, USA
| | - Gigi Galiana
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA
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Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [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: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
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Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
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Li H, Daniel AJ, Buchanan CE, Nery F, Morris DM, Li S, Huang Y, Sousa JA, Sourbron S, Mendichovszky IA, Thomas DL, Priest AN, Francis ST. Improvements in Between-Vendor MRI Harmonization of Renal T 2 Mapping using Stimulated Echo Compensation. J Magn Reson Imaging 2024. [PMID: 38380700 DOI: 10.1002/jmri.29282] [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: 11/09/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction. PURPOSE To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations. STUDY TYPE Prospective. SUBJECTS 8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy. FIELD STRENGTH/SEQUENCE 3T, multiecho spin-echo sequence. ASSESSMENT T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2 -weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements. STATISTICAL TESTS Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant. RESULTS In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney. DATA CONCLUSION Stimulated-echo correction reduces renal T2 variation across MR vendor platforms. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Hao Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | | | - Fábio Nery
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - David M Morris
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Shaohang Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
| | - João A Sousa
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Steven Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and School of Medicine, Nottingham, UK
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Holodov M, Markus I, Solomon C, Shahar S, Blumenfeld-Katzir T, Gepner Y, Ben-Eliezer N. Probing muscle recovery following downhill running using precise mapping of MRI T 2 relaxation times. Magn Reson Med 2023; 90:1990-2000. [PMID: 37345717 DOI: 10.1002/mrm.29765] [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: 09/09/2022] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE Postexercise recovery rate is a vital component of designing personalized training protocols and rehabilitation plans. Tracking exercise-induced muscle damage and recovery requires sensitive tools that can probe the muscles' state and composition noninvasively. METHODS Twenty-four physically active males completed a running protocol consisting of a 60-min downhill run on a treadmill at -10% incline and 65% of maximal heart rate. Quantitative mapping of MRI T2 was performed using the echo-modulation-curve algorithm before exercise, and at two time points: 1 h and 48 h after exercise. RESULTS T2 values increased by 2%-4% following exercise in the primary mover muscles and exhibited further elevation of 1% after 48 h. For the antagonist muscles, T2 values increased only at the 48-h time point (2%-3%). Statistically significant decrease in the SD of T2 values was found following exercise for all tested muscles after 1 h (16%-21%), indicating a short-term decrease in the heterogeneity of the muscle tissue. CONCLUSION MRI T2 relaxation time constitutes a useful quantitative marker for microstructural muscle damage, enabling region-specific identification for short-term and long-term systemic processes, and sensitive assessment of muscle recovery following exercise-induced muscle damage. The variability in T2 changes across different muscle groups can be attributed to their different role during downhill running, with immediate T2 elevation occurring in primary movers, followed by delayed elevation in both primary and antagonist muscle groups, presumably due to secondary damage caused by systemic processes.
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Affiliation(s)
- Maria Holodov
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Irit Markus
- Department of Epidemiology and Preventive Medicine, School of Public Health and Sylvan Adams Sports Institute, Tel-Aviv University, Tel-Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Shimon Shahar
- Center of AI and Data Science, Tel Aviv University, Tel Aviv, Israel
| | | | - Yftach Gepner
- Department of Epidemiology and Preventive Medicine, School of Public Health and Sylvan Adams Sports Institute, Tel-Aviv University, Tel-Aviv, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv, Israel
- Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, New York, USA
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Ben-Atya H, Freiman M. P 2T 2: A physically-primed deep-neural-network approach for robust T 2 distribution estimation from quantitative T 2-weighted MRI. Comput Med Imaging Graph 2023; 107:102240. [PMID: 37224742 DOI: 10.1016/j.compmedimag.2023.102240] [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: 01/13/2023] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 05/26/2023]
Abstract
Estimating T2 relaxation time distributions from multi-echo T2-weighted MRI (T2W) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN) based methods have been proposed to address the complex inverse problem of estimating T2 distributions from MRI data, but they are not yet robust enough for clinical data with low Signal-to-Noise ratio (SNR) and are highly sensitive to distribution shifts such as variations in echo-times (TE) used during acquisition. Consequently, their application is hindered in clinical practice and large-scale multi-institutional trials with heterogeneous acquisition protocols. We propose a physically-primed DNN approach, called P2T2, that incorporates the signal decay forward model in addition to the MRI signal into the DNN architecture to improve the accuracy and robustness of T2 distribution estimation. We evaluated our P2T2 model in comparison to both DNN-based methods and classical methods for T2 distribution estimation using 1D and 2D numerical simulations along with clinical data. Our model improved the baseline model's accuracy for low SNR levels (SNR<80) which are common in the clinical setting. Further, our model achieved a ∼35% improvement in robustness against distribution shifts in the acquisition process compared to previously proposed DNN models. Finally, Our P2T2 model produces the most detailed Myelin-Water fraction maps compared to baseline approaches when applied to real human MRI data. Our P2T2 model offers a reliable and precise means of estimating T2 distributions from MRI data and shows promise for use in large-scale multi-institutional trials with heterogeneous acquisition protocols. Our source code is available at: https://github.com/Hben-atya/P2T2-Robust-T2-estimation.git.
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Affiliation(s)
- Hadas Ben-Atya
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
| | - Moti Freiman
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
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Snyder J, Seres P, Wilman AH. Signal-to-noise ratio penalties from a loss of stimulated echoes when using slab-selective excitation in three-dimensional fast spin echo imaging with long echo trains. NMR IN BIOMEDICINE 2023; 36:e4881. [PMID: 36427186 DOI: 10.1002/nbm.4881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 06/16/2023]
Abstract
Three-dimensional fast spin echo imaging with long echo trains combines high resolution with reasonable acquisition times and reduced specific absorption rate due to low refocusing flip angles. Typically, an entire volume is encoded (nonselective excitation) or localization can be performed with slab select excitation, which uses a long 90° pulse for precise localization, followed by a preliminary nonselective 180° pulse bounded by spoiler gradients to destroy signal outside of the volume of interest. Subsequent flip angles in the train are nonselective and identical between the two methods. The inclusion of the initial selective pulse and spoiler gradients results in a signal-to-noise ratio (SNR) penalty for slab selection, beyond the slice-averaging dependence, arising from a loss of stimulated echoes. SNR differences are explored using Bloch equation simulations of a T2-weighted 96 echo train sequence with varying parameters including T2, T1, and B1+ and compared with phantom and in vivo brain, neck, and knee experiments. In vivo SNR measurements in the three regions showed a maximum decrease of selective SNR by 29% (gastrocnemius muscle), 25% (pons), and 22% (globus pallidus), despite similar experimental parameters to nonselective experiments. Decreased SNR was compounded by B1+ variation affecting prescribed flip angles with further smaller reductions with T2 and T1 times. In conclusion, the elimination of coherences via the preliminary nominal 180° pulse and spoiler gradients in addition to the extended echo timing from the long excitation pulse resulted in a reduction in SNR compared with the nonselective case. Consideration of the required SNR and chosen anatomy as well as sequence restrictions should be weighed before choosing slab-selective excitation.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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8
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Daniel G, Meirav G, Noam O, Tamar BK, Dvir R, Ricardo O, Noam BE. Fast and accurate T 2 mapping using Bloch simulations and low-rank plus sparse matrix decomposition. Magn Reson Imaging 2023; 98:66-75. [PMID: 36649808 DOI: 10.1016/j.mri.2023.01.007] [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: 11/21/2022] [Accepted: 01/08/2023] [Indexed: 01/15/2023]
Abstract
PURPOSE MRI's T2 relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T2-maps from highly undersampled multi-echo spin-echo (MESE) MRI data. METHODS Two new algorithms are presented: the first uses standard L + S approach, where both L and S are iteratively updated. The second technique, dubbed SPArse and fixed RanK (SPARK), uses a fixed-rank L, under the assumption that most MESE information is found in the L component and that this rank can be pre-calculated. The utility of these new techniques is demonstrated on in vivo brain and calf data at x2 to x6 acceleration factors. RESULTS Accelerated T2 maps showed improved accuracy compared to fully sampled ground truth maps, when using L + S and SPARK techniques vis-à-vis standard GRAPPA acceleration. CONCLUSION SPARK provides accurate T2 maps with increased robustness to the selection of reconstruction parameters making it suitable to a wide range of applications and facilitating the use of quantitative T2 information in clinical settings.
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Affiliation(s)
- Grzeda Daniel
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Galun Meirav
- Department of Computer Science and Applied Mathematics, Weitzman Institute of Science, Rehovot, Israel
| | - Omer Noam
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Radunsky Dvir
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Otazo Ricardo
- Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10016, USA
| | - Ben-Eliezer Noam
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel; Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY 10016, USA.
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9
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Nassar J, Trabelsi A, Amer R, Le Fur Y, Attarian S, Radunsky D, Blumenfeld-Katzir T, Greenspan H, Bendahan D, Ben-Eliezer N. Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T 2 analysis. NMR IN BIOMEDICINE 2023:e4947. [PMID: 37021657 DOI: 10.1002/nbm.4947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/13/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof-of-concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary-based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton-density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.
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Affiliation(s)
- Jannette Nassar
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Rula Amer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Shahram Attarian
- Reference Center for Neuromuscular Diseases and ALS, La Timone University Hospital, Aix-Marseille University, Marseille, France
- Inserm, GMGF, Aix Marseille University, Marseille, France
| | - Dvir Radunsky
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, New York, New York, USA
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10
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Snyder J, Seres P, Stobbe RW, Grenier JG, Smyth P, Blevins G, Wilman AH. Inline dual-echo T2 quantification in brain using a fast mapping reconstruction technique. NMR IN BIOMEDICINE 2023; 36:e4811. [PMID: 35934839 DOI: 10.1002/nbm.4811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 07/06/2022] [Accepted: 07/17/2022] [Indexed: 06/15/2023]
Abstract
T2 mapping from 2D proton density and T2-weighted images (PD-T2) using Bloch equation simulations can be time consuming and introduces a latency between image acquisition and T2 map production. A fast T2 mapping reconstruction method is investigated and compared with a previous modeling approach to reduce computation time and allow inline T2 maps on the MRI console. Brain PD-T2 images from five multiple sclerosis patients were used to compare T2 map reconstruction times between the new subtraction method and the Euclidean norm minimization technique. Bloch equation simulations were used to create the lookup table for decay curve matching in both cases. Agreement of the two techniques used Bland-Altman analysis for investigating individual subsets of data and all image points in the five volumes (meta-analysis). The subtraction method resulted in an average reduction of computation time for single slices from 134 s (minimization method) to 0.44 s. Comparing T2 values between the subtraction and minimization methods resulted in a confidence interval ranging from -0.06 to 0.06 ms (95% of values were within ± 0.06 ms between the techniques). Using identical reconstruction code based on the subtraction method, inline T2 maps were produced from PD-T2 images directly on the scanner console. The excellent agreement between the two methods permits the subtraction technique to be interchanged with the previous method, reducing computation time and allowing inline T2 map reconstruction based on Bloch simulations directly on the scanner.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Robert W Stobbe
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Justin G Grenier
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | - Penelope Smyth
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, Canada
| | - Gregg Blevins
- Department of Medicine, Division of Neurology, University of Alberta, Edmonton, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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11
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Stern N, Radunsky D, Blumenfeld‐Katzir T, Chechik Y, Solomon C, Ben‐Eliezer N. Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising. NMR IN BIOMEDICINE 2022; 35:e4807. [PMID: 35899528 PMCID: PMC9787782 DOI: 10.1002/nbm.4807] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T2 ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T2 relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T2 -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T2 values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T2 maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T2 maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.
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Affiliation(s)
- Neta Stern
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | - Dvir Radunsky
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | | | - Yigal Chechik
- Department of OrthopedicsShamir Medical CenterBe'er Ya'akovIsrael
- Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Chen Solomon
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
| | - Noam Ben‐Eliezer
- Department of Biomedical EngineeringTel Aviv UniversityIsrael
- Sagol School of NeuroscienceTel Aviv UniversityIsrael
- Center for Advanced Imaging Innovation and Research (CAIR)New York University School of MedicineNew YorkNew YorkUSA
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12
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Furman G, Meerovich V, Sokolovsky V, Xia Y, Salem S, Shavit T, Blumenfeld-Katzir T, Ben-Eliezer N. Determining the internal orientation, degree of ordering, and volume of elongated nanocavities by NMR: Application to studies of plant stem. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107258. [PMID: 35753185 PMCID: PMC9986720 DOI: 10.1016/j.jmr.2022.107258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 06/12/2022] [Accepted: 06/13/2022] [Indexed: 05/05/2023]
Abstract
This study investigates the fibril nanostructure of fresh celery samples by modeling the anisotropic behavior of the transverse relaxation time (T2) in nuclear magnetic resonance (NMR). Experimental results are interpreted within the framework of a previously developed theory, which was successfully used to model the nanostructures of several biological tissues as a set of water filled nanocavities, hence explaining the anisotropy the T2 relaxation time in vivo. An important feature of this theory is to determine the degree of orientational ordering of the nanocavities, their characteristic volume, and their average direction with respect to the macroscopic sample. Results exhibit good agreement between theory and experimental data, which are, moreover, supported by optical microscopic resolution. The quantitative NMR approach presented herein can be potentially used to determine the internal ordering of biological tissues noninvasively.
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Affiliation(s)
- Gregory Furman
- Physics Department, Ben Gurion University of the Negev, Beer Sheva, Israel.
| | - Victor Meerovich
- Physics Department, Ben Gurion University of the Negev, Beer Sheva, Israel
| | | | - Yang Xia
- Physics Department, Oakland University, Rochester, MI, USA
| | - Sarah Salem
- Physics Department, Oakland University, Rochester, MI, USA
| | - Tamar Shavit
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel; Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, NY, USA
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13
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Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation. Bioengineering (Basel) 2022; 9:bioengineering9070315. [PMID: 35877366 PMCID: PMC9312115 DOI: 10.3390/bioengineering9070315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/29/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.
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14
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Bnaiahu N, Omer N, Wilczynski E, Levy S, Blumenfeld-Katzir T, Ben-Eliezer N. Correcting for imaging gradients-related bias of T 2 relaxation times at high-resolution MRI. Magn Reson Med 2022; 88:1806-1817. [PMID: 35666831 PMCID: PMC9544944 DOI: 10.1002/mrm.29319] [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: 01/04/2022] [Revised: 04/15/2022] [Accepted: 05/11/2022] [Indexed: 11/16/2022]
Abstract
Purpose High‐resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T2 relaxation times (qT2) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High‐resolution T2 mapping, however, suffers from severe underestimation of T2 values due to molecular diffusion. This affects both single‐echo and multi‐echo spin echo (SSE and MESE), on top of the well‐known contamination of MESE signals by stimulated echoes, and especially on high‐field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners. Methods Diffusion bias due to imaging gradients was analyzed by quantifying the effective b‐value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T2‐diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner. Results Underestimation of T2 values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T2 values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T2 values from −33.5 ± 20.5% to −0.1 ± 3.6%. Conclusions A new joint T2‐diffusion reconstruction algorithm is able to negate imaging gradient–related underestimation of T2 values, leading to reliable mapping of T2 values at high resolutions. Click here for author‐reader discussions
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Affiliation(s)
- Natalie Bnaiahu
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Noam Omer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Ella Wilczynski
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Shir Levy
- School of Chemistry, Tel Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, USA
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15
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Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers (Basel) 2022; 14:cancers14071626. [PMID: 35406399 PMCID: PMC8997011 DOI: 10.3390/cancers14071626] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 12/26/2022] Open
Abstract
Simple Summary Magnetic Resonance Imaging (MRI) is a consolidated imaging tool for the multiparametric assessment of tissues in various pathologies from degenerative and inflammatory diseases to cancer. In recent years, the continuous technological evolution of the equipment has led to the development of sequences that provide not only anatomical but also functional and metabolic information. In addition, there is a growing and emerging field of research in clinical applications using MRI to exploit the diagnostic and therapeutic capabilities of nanocompounds. This review illustrates the application of the most advanced magnetic resonance techniques in the field of nanomedicine. Abstract In the last decades, nanotechnology has been used in a wide range of biomedical applications, both diagnostic and therapeutic. In this scenario, imaging techniques represent a fundamental tool to obtain information about the properties of nanoconstructs and their interactions with the biological environment in preclinical and clinical settings. This paper reviews the state of the art of the application of magnetic resonance imaging in the field of nanomedicine, as well as the use of nanoparticles as diagnostic and therapeutic tools, especially in cancer, including the characteristics that hinder the use of nanoparticles in clinical practice.
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16
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Shpringer G, Bendahan D, Ben-Eliezer N. Accelerated reconstruction of dictionary-based T 2 relaxation maps based on dictionary compression and gradient descent search algorithms. Magn Reson Imaging 2021; 87:56-66. [PMID: 34973389 DOI: 10.1016/j.mri.2021.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/19/2021] [Accepted: 12/22/2021] [Indexed: 11/18/2022]
Abstract
Background Quantitative T2-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones. Purpose Although the EMC technique has shown to produce accurate T2 maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T2 relaxation maps. Methods This work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T2 relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique. Results Gradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler. Conclusions Postprocessing of EMC-based T2 relaxation maps can be accelerated without impairing the accuracy of the ensuing T2 values.
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Affiliation(s)
- Guy Shpringer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - David Bendahan
- Aix Marseille University, CNRS UMR 7339, CRMBM, Marseille, France
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, USA.
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17
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Omer N, Galun M, Stern N, Blumenfeld-Katzir T, Ben-Eliezer N. Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features. Magn Reson Med 2021; 87:2521-2535. [PMID: 34958690 DOI: 10.1002/mrm.29125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 01/10/2023]
Abstract
PURPOSE Multicomponent analysis of MRI T2 relaxation time (mcT2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. METHODS Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS Validations are shown for computer-generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION We conclude that studying global tissue motifs prior to performing voxel-wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.
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Affiliation(s)
- Noam Omer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Meirav Galun
- Department of Computer Science and Applied Mathematics, Weitzman Institute of Science, Rehovot, Israel
| | - Neta Stern
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, New York, USA
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18
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Guenthner C, Amthor T, Doneva M, Kozerke S. A unifying view on extended phase graphs and Bloch simulations for quantitative MRI. Sci Rep 2021; 11:21289. [PMID: 34711847 PMCID: PMC8553818 DOI: 10.1038/s41598-021-00233-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/05/2021] [Indexed: 12/16/2022] Open
Abstract
Quantitative MRI methods and learning-based algorithms require exact forward simulations. One critical factor to correctly describe magnetization dynamics is the effect of slice-selective RF pulses. While contemporary simulation techniques correctly capture their influence, they only provide final magnetization distributions, require to be run for each parameter set separately, and make it hard to derive general theoretical conclusions and to generate a fundamental understanding of echo formation in the presence of slice-profile effects. This work aims to provide a mathematically exact framework, which is equally intuitive as extended phase graphs (EPGs), but also considers slice-profiles through their natural spatial representation. We show, through an analytical, hybrid Bloch-EPG formalism, that the spatially-resolved EPG approach allows to exactly predict the signal dependency on off-resonance, spoiling moment, microscopic dephasing, and echo time. We also demonstrate that our formalism allows to use the same phase graph to simulate both gradient-spoiled and balanced SSFP-based MR sequences. We present a derivation of the formalism and identify the connection to existing methods, i.e. slice-selective Bloch, slice-selective EPG, and the partitioned EPG. As a use case, the proposed hybrid Bloch-EPG framework is applied to MR Fingerprinting.
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Affiliation(s)
- Christian Guenthner
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
- Philips Research, Hamburg, Germany.
| | | | | | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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