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\n \n \n\n \n \n \n \n \n \n Quantitative T1 MRI.\n \n \n \n \n\n\n \n Boudreau, M.; Keenan, K. E.; and Stikov, N.\n\n\n \n\n\n\n Technical Report NeuroLibre Reproducible Preprints, November 2023.\n \n\n\n\n
\n\n\n\n \n \n![\"Quantitative](\"//bibbase.org/img/filetypes/link.svg\"\n\t "\\"Paper\\"\n\t")
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\n\n\n@techreport{boudreau_quantitative_2023,\n\ttype = {preprint},\n\ttitle = {Quantitative {T1} {MRI}},\n\turl = {https://neurolibre.org/papers/10.55458/neurolibre.00019},\n\tabstract = {This NeuroLibre Reproducible preprint is an interactive tutorial on quantitative T1 mapping MRI. It is an interactive version of two subsections of the chapter “Quantitative T1 and T1r Mapping” in the book Quantitative Magnetic Resonance Imaging (Boudreau et al., 2020).},\n\tlanguage = {en},\n\turldate = {2023-11-24},\n\tinstitution = {NeuroLibre Reproducible Preprints},\n\tauthor = {Boudreau, Mathieu and Keenan, Kathryn E. and Stikov, Nikola},\n\tmonth = nov,\n\tyear = {2023},\n\tdoi = {10.55458/neurolibre.00019},\n}\n\n\n\n This NeuroLibre Reproducible preprint is an interactive tutorial on quantitative T1 mapping MRI. It is an interactive version of two subsections of the chapter “Quantitative T1 and T1r Mapping” in the book Quantitative Magnetic Resonance Imaging (Boudreau et al., 2020).\n
\n\n\n\n \n\n \n \n \n \n \n \n Steps on the Path to Clinical Translation: A workshop by the British and Irish Chapter of the ISMRM.\n \n \n \n \n\n\n \n Hubbard Cristinacce, P. L.; Markus, J. E.; Punwani, S.; Mills, R.; Lopez, M. Y.; Grech-Sollars, M.; Fasano, F.; Waterton, J. C.; Thrippleton, M. J.; Hall, M. G.; O'Connor, J. P. B.; Francis, S. T.; Statton, B.; Murphy, K.; So, P.; and Hyare, H.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 90(3): 1130–1136. 2023.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29704\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{hubbard_cristinacce_steps_2023,\n\ttitle = {Steps on the {Path} to {Clinical} {Translation}: {A} workshop by the {British} and {Irish} {Chapter} of the {ISMRM}},\n\tvolume = {90},\n\tcopyright = {© 2023 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.},\n\tissn = {1522-2594},\n\tshorttitle = {Steps on the {Path} to {Clinical} {Translation}},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.29704},\n\tdoi = {10.1002/mrm.29704},\n\tabstract = {The British and Irish Chapter of the International Society for Magnetic Resonance in Medicine (BIC-ISMRM) held a workshop entitled “Steps on the path to clinical translation” in Cardiff, UK, on 7th September 2022. The aim of the workshop was to promote discussion within the MR community about the problems and potential solutions for translating quantitative MR (qMR) imaging and spectroscopic biomarkers into clinical application and drug studies. Invited speakers presented the perspectives of radiologists, radiographers, clinical physicists, vendors, imaging Contract/Clinical Research Organizations (CROs), open science networks, metrologists, imaging networks, and those developing consensus methods. A round-table discussion was held in which workshop participants discussed a range of questions pertinent to clinical translation of qMR imaging and spectroscopic biomarkers. Each group summarized their findings via three main conclusions and three further questions. These questions were used as the basis of an online survey of the broader UK MR community.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-08-03},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Hubbard Cristinacce, Penny L. and Markus, Julia E. and Punwani, Shonit and Mills, Rebecca and Lopez, Maria Yanez and Grech-Sollars, Matthew and Fasano, Fabrizio and Waterton, John C. and Thrippleton, Michael J. and Hall, Matt G. and O'Connor, James P. B. and Francis, Susan T. and Statton, Ben and Murphy, Kevin and So, Po-Wah and Hyare, Harpreet},\n\tyear = {2023},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29704},\n\tkeywords = {Imaging biomarkers, clinical translation, consensus, quantitative MRI, survey},\n\tpages = {1130--1136},\n}\n\n\n\n The British and Irish Chapter of the International Society for Magnetic Resonance in Medicine (BIC-ISMRM) held a workshop entitled “Steps on the path to clinical translation” in Cardiff, UK, on 7th September 2022. The aim of the workshop was to promote discussion within the MR community about the problems and potential solutions for translating quantitative MR (qMR) imaging and spectroscopic biomarkers into clinical application and drug studies. Invited speakers presented the perspectives of radiologists, radiographers, clinical physicists, vendors, imaging Contract/Clinical Research Organizations (CROs), open science networks, metrologists, imaging networks, and those developing consensus methods. A round-table discussion was held in which workshop participants discussed a range of questions pertinent to clinical translation of qMR imaging and spectroscopic biomarkers. Each group summarized their findings via three main conclusions and three further questions. These questions were used as the basis of an online survey of the broader UK MR community.\n
\n\n\n\n \n\n \n \n \n \n \n Magnetisation Transfer effects on T1 and T2 values in MR Fingerprinting.\n \n \n \n\n\n \n Kukran, S.; Dragonu, I.; Statton, B.; Allen, J.; Lally, P.; Quest, R.; Bangerter, N.; Koh, D.; Orton, M.; and Grech, M.\n\n\n \n\n\n\n In Proceedings of the 31th Annual Meeting of the International Society of Magnetic Resonance in Medicine, 2023. \n \n\n\n\n
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\n\n\n@inproceedings{kukran_magnetisation_2023,\n\ttitle = {Magnetisation {Transfer} effects on {T1} and {T2} values in {MR} {Fingerprinting}},\n\tabstract = {Methods A FISP 2D cartesian MRF sequence with and\nwithout off resonance pulses before every readout was developed in house with\nvarying flip angles and repetition times based on schedules reported by Jiang\net al5. After an adiabatic inversion pulse, a series of FISP\nacquisitions were acquired with a sinusoidal variation of flip angles (FA) and\nrepetition times (TR) in a Perlin noise pattern. The FOV was 300 and the acquisition matrix was 128 x 128. 64 lines of k space per image were acquired\nwith GRAPPA-2 acceleration. A schematic of the pulse sequence is included in\nFigure 1. A total of 828 contrasts with a slice\nthickness of 5mm were acquired to reconstruct a timeseries of complex images. The\noff-resonance pulse was 180° with a frequency offset of 2000Hz. Data acquisition with and without the\noff-resonance pulses was performed on a Siemens MAGNETOM Prisma 3 Tesla system\n(Siemens Healthineers, Erlangen Germany). Initial investigations were performed\non the NIST phantom, a crosslinked bovine-serum\nalbumin (BSA) MT phantom, and in a single healthy volunteer as part of an\nethically approved study after obtaining informed consent.\nA dictionary was generated using a single-pool\nextended phase graph (EPG) model developed by Malik et al6 for T1\nvalues 10-4500ms, T2 values 2-3000ms and B1+ values 0.8 to 1.2. To\nincorporate slice profile effects7-9 the effective imaging flip\nangle was simulated for each prescribed sinc RF pulse and slice selection\ngradient profile to generate fingerprints at 11 discrete points across the 5mm\nimaging slice, which were then summed and normalised for matching. The B1\nwas preselected via comparison to a Siemens turbo-flash map. Fingerprints for\nevery voxel were extracted and matched to the closest dictionary entry using a maximum\ndot product search, and corresponding T1 and T2 maps were reconstructed.\nResults and Discussion In Figure 2, signal evolutions for single\nvoxels acquired with and without off resonance in all three scanning subjects\nare shown. No magnetisation transfer takes place in the liquid NIST phantom\nbecause only free protons are present. In all other voxels, off-resonance pulses saturate bound protons and areas with significant bound and free proton\ninteractions, reducing the signal magnitude as only free protons are measured. This\nalso changes the shape of the signal evolution, and leads to different matches\nfor normalised fingerprints, as shown in Figure 3. The resulting T1 and T2 maps are shown in Figures\n4 and 5 respectively. In this experiment, applying off resonance pulses\nthroughout the acquisition caused the observed T1 to decrease. This behaviour\nwas unexpected and opposite to what was observed in our previous experiment4.\nIt is also in the opposite direction to changes in T1 observed when MT effects on a fingerprinting acquisition were\nsimulated explicitly by Hilbert et al10. T2 values increased when off-resonance pulses were\napplied. this is similar to what was observed when MT effects were simulated explicitly10.\nConclusion},\n\tlanguage = {en},\n\tbooktitle = {Proceedings of the 31th {Annual} {Meeting} of the {International} {Society} of {Magnetic} {Resonance} in {Medicine}},\n\tauthor = {Kukran, Simran and Dragonu, Iulius and Statton, Ben and Allen, Jack and Lally, Pete and Quest, Rebecca and Bangerter, Neal and Koh, Dow-Mu and Orton, Matthew and Grech, Matthew},\n\tyear = {2023},\n}\n\n\n\n Methods A FISP 2D cartesian MRF sequence with and without off resonance pulses before every readout was developed in house with varying flip angles and repetition times based on schedules reported by Jiang et al5. After an adiabatic inversion pulse, a series of FISP acquisitions were acquired with a sinusoidal variation of flip angles (FA) and repetition times (TR) in a Perlin noise pattern. The FOV was 300 and the acquisition matrix was 128 x 128. 64 lines of k space per image were acquired with GRAPPA-2 acceleration. A schematic of the pulse sequence is included in Figure 1. A total of 828 contrasts with a slice thickness of 5mm were acquired to reconstruct a timeseries of complex images. The off-resonance pulse was 180° with a frequency offset of 2000Hz. Data acquisition with and without the off-resonance pulses was performed on a Siemens MAGNETOM Prisma 3 Tesla system (Siemens Healthineers, Erlangen Germany). Initial investigations were performed on the NIST phantom, a crosslinked bovine-serum albumin (BSA) MT phantom, and in a single healthy volunteer as part of an ethically approved study after obtaining informed consent. A dictionary was generated using a single-pool extended phase graph (EPG) model developed by Malik et al6 for T1 values 10-4500ms, T2 values 2-3000ms and B1+ values 0.8 to 1.2. To incorporate slice profile effects7-9 the effective imaging flip angle was simulated for each prescribed sinc RF pulse and slice selection gradient profile to generate fingerprints at 11 discrete points across the 5mm imaging slice, which were then summed and normalised for matching. The B1 was preselected via comparison to a Siemens turbo-flash map. Fingerprints for every voxel were extracted and matched to the closest dictionary entry using a maximum dot product search, and corresponding T1 and T2 maps were reconstructed. Results and Discussion In Figure 2, signal evolutions for single voxels acquired with and without off resonance in all three scanning subjects are shown. No magnetisation transfer takes place in the liquid NIST phantom because only free protons are present. In all other voxels, off-resonance pulses saturate bound protons and areas with significant bound and free proton interactions, reducing the signal magnitude as only free protons are measured. This also changes the shape of the signal evolution, and leads to different matches for normalised fingerprints, as shown in Figure 3. The resulting T1 and T2 maps are shown in Figures 4 and 5 respectively. In this experiment, applying off resonance pulses throughout the acquisition caused the observed T1 to decrease. This behaviour was unexpected and opposite to what was observed in our previous experiment4. It is also in the opposite direction to changes in T1 observed when MT effects on a fingerprinting acquisition were simulated explicitly by Hilbert et al10. T2 values increased when off-resonance pulses were applied. this is similar to what was observed when MT effects were simulated explicitly10. Conclusion\n
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\n \n \n\n \n \n \n \n \n \n Challenges in ensuring the generalizability of image quantitation methods for MRI.\n \n \n \n \n\n\n \n Keenan, K. E.; Delfino, J. G.; Jordanova, K. V.; Poorman, M. E.; Chirra, P.; Chaudhari, A. S.; Baessler, B.; Winfield, J.; Viswanath, S. E.; and deSouza , N. M.\n\n\n \n\n\n\n Medical Physics, 49(4): 2820–2835. 2022.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15195\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{keenan_challenges_2022,\n\ttitle = {Challenges in ensuring the generalizability of image quantitation methods for {MRI}},\n\tvolume = {49},\n\tcopyright = {© 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.},\n\tissn = {2473-4209},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.15195},\n\tdoi = {10.1002/mp.15195},\n\tabstract = {Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-10-23},\n\tjournal = {Medical Physics},\n\tauthor = {Keenan, Kathryn E. and Delfino, Jana G. and Jordanova, Kalina V. and Poorman, Megan E. and Chirra, Prathyush and Chaudhari, Akshay S. and Baessler, Bettina and Winfield, Jessica and Viswanath, Satish E. and deSouza, Nandita M.},\n\tyear = {2022},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15195},\n\tkeywords = {magnetic resonance imaging, multiparametric MRI, quantitative MRI, radiomics},\n\tpages = {2820--2835},\n}\n\n\n\n Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.\n
\n\n\n\n \n\n \n \n \n \n \n \n The perfect qMR machine: Measurement variance much less than biological variance.\n \n \n \n \n\n\n \n Tofts, P. S.\n\n\n \n\n\n\n Physica Medica, 104: 145–148. December 2022.\n \n\n\n\n
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\n\n\n\n@article{tofts_perfect_2022,\n\ttitle = {The perfect {qMR} machine: {Measurement} variance much less than biological variance},\n\tvolume = {104},\n\tissn = {11201797},\n\tshorttitle = {The perfect {qMR} machine},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S1120179722020749},\n\tdoi = {10.1016/j.ejmp.2022.10.013},\n\tlanguage = {en},\n\turldate = {2023-06-09},\n\tjournal = {Physica Medica},\n\tauthor = {Tofts, Paul S.},\n\tmonth = dec,\n\tyear = {2022},\n\tpages = {145--148},\n}\n\n\n\n \n\n \n \n \n \n \n Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking.\n \n \n \n\n\n \n Cao, T.; Ma, S.; Wang, N.; Gharabaghi, S.; Xie, Y.; Fan, Z.; Hogg, E.; Wu, C.; Han, F.; Tagliati, M.; Haacke, E. M.; Christodoulou, A. G.; and Li, D.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 87(3): 1375–1389. March 2022.\n \n\n\n\n
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\n\n\n@article{cao_three-dimensional_2022,\n\ttitle = {Three-dimensional simultaneous brain mapping of {T1}, {T2}, {T2}∗ and magnetic susceptibility with {MR} {Multitasking}},\n\tvolume = {87},\n\tissn = {1522-2594},\n\tdoi = {10.1002/mrm.29059},\n\tabstract = {PURPOSE: To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T2∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan.\nMETHODS: The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T2∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined.\nRESULTS: High quality, co-registered T1 , T2 , and T2∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 {\\textgreater} 0.98) with the reference measurements. Despite the significant differences of T1 (p {\\textless} .001), T2 (p = .002), and T2∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images.\nCONCLUSION: The MR Multitasking-based 3D brain mapping of T1 , T2 , T2∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.},\n\tlanguage = {eng},\n\tnumber = {3},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Cao, Tianle and Ma, Sen and Wang, Nan and Gharabaghi, Sara and Xie, Yibin and Fan, Zhaoyang and Hogg, Elliot and Wu, Chaowei and Han, Fei and Tagliati, Michele and Haacke, E. Mark and Christodoulou, Anthony G. and Li, Debiao},\n\tmonth = mar,\n\tyear = {2022},\n\tpmid = {34708438},\n\tpmcid = {PMC8776611},\n\tkeywords = {Brain, Brain Mapping, Humans, MR Multitasking, MR studies, Magnetic Phenomena, Magnetic Resonance Imaging, Phantoms, Imaging, brain, multiparametric mapping, quantitative MRI},\n\tpages = {1375--1389},\n}\n\n\n\n PURPOSE: To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T2∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan. METHODS: The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T2∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined. RESULTS: High quality, co-registered T1 , T2 , and T2∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 \\textgreater 0.98) with the reference measurements. Despite the significant differences of T1 (p \\textless .001), T2 (p = .002), and T2∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images. CONCLUSION: The MR Multitasking-based 3D brain mapping of T1 , T2 , T2∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.\n
\n\n\n\n \n\n \n \n \n \n \n \n Mitigation of Magnetisation Transfer Effects using Off-Resonance Pulses in MR Fingerprinting.\n \n \n \n \n\n\n \n Kukran, S.; Dragonu, I.; Statton, B.; Allen, J.; Lally, P.; Quest, R.; Bangerter, N.; Koh, D.; Orton, M.; and Grech-Sollars, M.\n\n\n \n\n\n\n In Proceedings of the 30th Annual Meeting of the International Society of Magnetic Resonance in Medicine, 2022. \n \n\n\n\n
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\n\n\n\n@inproceedings{kukran_mitigation_2022,\n\ttitle = {Mitigation of {Magnetisation} {Transfer} {Effects} using {Off}-{Resonance} {Pulses} in {MR} {Fingerprinting}},\n\turl = {https://submissions.mirasmart.com/ISMRM2022/Itinerary/Files/PDFFiles/2598.html},\n\tbooktitle = {Proceedings of the 30th {Annual} {Meeting} of the {International} {Society} of {Magnetic} {Resonance} in {Medicine}},\n\tauthor = {Kukran, Simran and Dragonu, Iulius and Statton, Ben and Allen, Jack and Lally, Pete and Quest, Rebecca and Bangerter, Neal and Koh, Dow-Mu and Orton, Matthew and Grech-Sollars, Matthew},\n\tyear = {2022},\n}\n\n\n\n \n\n \n \n \n \n \n \n Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques.\n \n \n \n \n\n\n \n Jara, H.; Sakai, O.; Farrher, E.; Oros-Peusquens, A.; Shah, N. J.; Alsop, D. C.; and Keenan, K. E.\n\n\n \n\n\n\n Radiology, 305(1): 5–18. October 2022.\n Publisher: Radiological Society of North America\n\n\n\n
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\n\n\n@article{jara_primary_2022,\n\ttitle = {Primary {Multiparametric} {Quantitative} {Brain} {MRI}: {State}-of-the-{Art} {Relaxometric} and {Proton} {Density} {Mapping} {Techniques}},\n\tvolume = {305},\n\tissn = {0033-8419},\n\tshorttitle = {Primary {Multiparametric} {Quantitative} {Brain} {MRI}},\n\turl = {https://pubs.rsna.org/doi/10.1148/radiol.211519},\n\tdoi = {10.1148/radiol.211519},\n\tabstract = {This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile (“free”) and bound (“motion-restricted”) proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling.\n\n© RSNA, 2022},\n\tnumber = {1},\n\turldate = {2023-09-12},\n\tjournal = {Radiology},\n\tauthor = {Jara, Hernán and Sakai, Osamu and Farrher, Ezequiel and Oros-Peusquens, Ana-Maria and Shah, N. Jon and Alsop, David C. and Keenan, Kathryn E.},\n\tmonth = oct,\n\tyear = {2022},\n\tnote = {Publisher: Radiological Society of North America},\n\tpages = {5--18},\n}\n\n\n\n This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile (“free”) and bound (“motion-restricted”) proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling. © RSNA, 2022\n
\n\n\n\n \n\n \n \n \n \n \n Phase-based fast 3D high-resolution quantitative T2 MRI in 7 T human brain imaging.\n \n \n \n\n\n \n Seginer, A.; and Schmidt, R.\n\n\n \n\n\n\n Scientific Reports, 12(1): 14088. August 2022.\n \n\n\n\n
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\n\n\n@article{seginer_phase-based_2022,\n\ttitle = {Phase-based fast {3D} high-resolution quantitative {T2} {MRI} in 7 {T} human brain imaging},\n\tvolume = {12},\n\tissn = {2045-2322},\n\tdoi = {10.1038/s41598-022-17607-z},\n\tabstract = {Magnetic resonance imaging (MRI) is a powerful and versatile technique that offers a range of physiological, diagnostic, structural, and functional measurements. One of the most widely used basic contrasts in MRI diagnostics is transverse relaxation time (T2)-weighted imaging, but it provides only qualitative information. Realizing quantitative high-resolution T2 mapping is imperative for the development of personalized medicine, as it can enable the characterization of diseases progression. While ultra-high-field (≥ 7 T) MRI offers the means to gain new insights by increasing the spatial resolution, implementing fast quantitative T2 mapping cannot be achieved without overcoming the increased power deposition and radio frequency (RF) field inhomogeneity at ultra-high-fields. A recent study has demonstrated a new phase-based T2 mapping approach based on fast steady-state acquisitions. We extend this new approach to ultra-high field MRI, achieving quantitative high-resolution 3D T2 mapping at 7 T while addressing RF field inhomogeneity and utilizing low flip angle pulses; overcoming two main ultra-high field challenges. The method is based on controlling the coherent transverse magnetization in a steady-state gradient echo acquisition; achieved by utilizing low flip angles, a specific phase increment for the RF pulses, and short repetition times. This approach simultaneously extracts both T2 and RF field maps from the phase of the signal. Prior to in vivo experiments, the method was assessed using a 3D head-shaped phantom that was designed to model the RF field distribution in the brain. Our approach delivers fast 3D whole brain images with submillimeter resolution without requiring special hardware, such as multi-channel transmit coil, thus promoting high usability of the ultra-high field MRI in clinical practice.},\n\tlanguage = {eng},\n\tnumber = {1},\n\tjournal = {Scientific Reports},\n\tauthor = {Seginer, Amir and Schmidt, Rita},\n\tmonth = aug,\n\tyear = {2022},\n\tpmid = {35982143},\n\tpmcid = {PMC9388657},\n\tkeywords = {Brain, Brain Mapping, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Phantoms, Imaging},\n\tpages = {14088},\n}\n\n\n\n Magnetic resonance imaging (MRI) is a powerful and versatile technique that offers a range of physiological, diagnostic, structural, and functional measurements. One of the most widely used basic contrasts in MRI diagnostics is transverse relaxation time (T2)-weighted imaging, but it provides only qualitative information. Realizing quantitative high-resolution T2 mapping is imperative for the development of personalized medicine, as it can enable the characterization of diseases progression. While ultra-high-field (≥ 7 T) MRI offers the means to gain new insights by increasing the spatial resolution, implementing fast quantitative T2 mapping cannot be achieved without overcoming the increased power deposition and radio frequency (RF) field inhomogeneity at ultra-high-fields. A recent study has demonstrated a new phase-based T2 mapping approach based on fast steady-state acquisitions. We extend this new approach to ultra-high field MRI, achieving quantitative high-resolution 3D T2 mapping at 7 T while addressing RF field inhomogeneity and utilizing low flip angle pulses; overcoming two main ultra-high field challenges. The method is based on controlling the coherent transverse magnetization in a steady-state gradient echo acquisition; achieved by utilizing low flip angles, a specific phase increment for the RF pulses, and short repetition times. This approach simultaneously extracts both T2 and RF field maps from the phase of the signal. Prior to in vivo experiments, the method was assessed using a 3D head-shaped phantom that was designed to model the RF field distribution in the brain. Our approach delivers fast 3D whole brain images with submillimeter resolution without requiring special hardware, such as multi-channel transmit coil, thus promoting high usability of the ultra-high field MRI in clinical practice.\n
\n\n\n\n \n\n \n \n \n \n \n Artificial intelligence in cardiac magnetic resonance fingerprinting.\n \n \n \n\n\n \n Velasco, C.; Fletcher, T. J.; Botnar, R. M.; and Prieto, C.\n\n\n \n\n\n\n Frontiers in Cardiovascular Medicine, 9: 1009131. 2022.\n \n\n\n\n
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\n\n\n@article{velasco_artificial_2022,\n\ttitle = {Artificial intelligence in cardiac magnetic resonance fingerprinting},\n\tvolume = {9},\n\tissn = {2297-055X},\n\tdoi = {10.3389/fcvm.2022.1009131},\n\tabstract = {Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.},\n\tlanguage = {eng},\n\tjournal = {Frontiers in Cardiovascular Medicine},\n\tauthor = {Velasco, Carlos and Fletcher, Thomas J. and Botnar, René M. and Prieto, Claudia},\n\tyear = {2022},\n\tpmid = {36204566},\n\tpmcid = {PMC9530662},\n\tkeywords = {artificial intelligence (AI), cardiac MRF, cardiac magnetic resonance (CMR), magnetic resonance fingerprinting (MRF), multiparametric imaging},\n\tpages = {1009131},\n}\n\n\n\n Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.\n
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\n\n \n \n 2021\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Joint multi‐field T \\textlessspan style=\"font-variant:small-caps;\"\\textgreater $_{\\textrm{1}}$ \\textless/span\\textgreater quantification for fast field‐cycling MRI.\n \n \n \n \n\n\n \n Bödenler, M.; Maier, O.; Stollberger, R.; Broche, L. M.; Ross, P. J.; MacLeod, M.; and Scharfetter, H.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 86(4): 2049–2063. October 2021.\n \n\n\n\n
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\n\n\n@article{bodenler_joint_2021,\n\ttitle = {Joint multi‐field {T} {\\textless}span style="font-variant:small-caps;"{\\textgreater} $_{\\textrm{1}}$ {\\textless}/span{\\textgreater} quantification for fast field‐cycling {MRI}},\n\tvolume = {86},\n\tissn = {0740-3194, 1522-2594},\n\tshorttitle = {Joint multi‐field {T} {\\textless}span style="font-variant},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.28857},\n\tdoi = {10.1002/mrm.28857},\n\tabstract = {Purpose: Recent developments in hardware design enable the use of fast field- cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues for in vivo characterizations of pathologies but at the expense of longer acquisition times. To mitigate this, we propose a model-b ased reconstruction method that fully exploits the high information redundancy offered by FFC methods.\nMethods: The proposed model-based approach uses joint spatial information from all fields by means of a Frobenius -total generalized variation regularization. The algorithm was tested on brain stroke images, both simulated and acquired from FFC patients scans using an FFC spin echo sequences. The results are compared to three non-linear least squares fits with progressively increasing complexity.\nResults: The proposed method shows excellent abilities to remove noise while maintaining sharp image features with large signal-to-noise ratio gains at low-field images, clearly outperforming the reference approach. Especially patient data show huge improvements in visual appearance over all fields.\nConclusion: The proposed reconstruction technique largely improves FFC image quality, further pushing this new technology toward clinical standards.},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2023-09-15},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Bödenler, Markus and Maier, Oliver and Stollberger, Rudolf and Broche, Lionel M. and Ross, P. James and MacLeod, Mary‐Joan and Scharfetter, Hermann},\n\tmonth = oct,\n\tyear = {2021},\n\tpages = {2049--2063},\n}\n\n\n\n Purpose: Recent developments in hardware design enable the use of fast field- cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues for in vivo characterizations of pathologies but at the expense of longer acquisition times. To mitigate this, we propose a model-b ased reconstruction method that fully exploits the high information redundancy offered by FFC methods. Methods: The proposed model-based approach uses joint spatial information from all fields by means of a Frobenius -total generalized variation regularization. The algorithm was tested on brain stroke images, both simulated and acquired from FFC patients scans using an FFC spin echo sequences. The results are compared to three non-linear least squares fits with progressively increasing complexity. Results: The proposed method shows excellent abilities to remove noise while maintaining sharp image features with large signal-to-noise ratio gains at low-field images, clearly outperforming the reference approach. Especially patient data show huge improvements in visual appearance over all fields. Conclusion: The proposed reconstruction technique largely improves FFC image quality, further pushing this new technology toward clinical standards.\n
\n\n\n\n \n\n \n \n \n \n \n Efficient T2 mapping with blip-up/down EPI and gSlider-SMS (T2 -BUDA-gSlider).\n \n \n \n\n\n \n Cao, X.; Wang, K.; Liao, C.; Zhang, Z.; Srinivasan Iyer, S.; Chen, Z.; Lo, W.; Liu, H.; He, H.; Setsompop, K.; Zhong, J.; and Bilgic, B.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 86(4): 2064–2075. October 2021.\n \n\n\n\n
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\n\n\n@article{cao_efficient_2021,\n\ttitle = {Efficient {T2} mapping with blip-up/down {EPI} and {gSlider}-{SMS} ({T2} -{BUDA}-{gSlider})},\n\tvolume = {86},\n\tissn = {1522-2594},\n\tdoi = {10.1002/mrm.28872},\n\tabstract = {PURPOSE: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity.\nMETHODS: A T2 blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T2 -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T2 quantification.\nRESULTS: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated.\nCONCLUSION: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at {\\textasciitilde}1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.},\n\tlanguage = {eng},\n\tnumber = {4},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Cao, Xiaozhi and Wang, Kang and Liao, Congyu and Zhang, Zijing and Srinivasan Iyer, Siddharth and Chen, Zhifeng and Lo, Wei-Ching and Liu, Huafeng and He, Hongjian and Setsompop, Kawin and Zhong, Jianhui and Bilgic, Berkin},\n\tmonth = oct,\n\tyear = {2021},\n\tpmid = {34046924},\n\tpmcid = {PMC8295207},\n\tkeywords = {BUDA, Brain, Brain Mapping, Echo-Planar Imaging, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, T2 map, gSlider, structured low rank},\n\tpages = {2064--2075},\n}\n\n\n\n PURPOSE: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. METHODS: A T2 blip-up/down EPI acquisition with generalized slice-dithered enhanced resolution (T2 -BUDA-gSlider) is proposed. A RF-encoded multi-slab spin-echo (SE) EPI acquisition with multiple TEs was developed to obtain high SNR efficiency with reduced TR. This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and used for T2 quantification. RESULTS: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. CONCLUSION: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications.\n
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\n \n \n\n \n \n \n \n \n \n Quantitative T2 mapping using accelerated 3D stack-of-spiral gradient echo readout.\n \n \n \n \n\n\n \n R, Z.; D, Z.; and Q, Q.\n\n\n \n\n\n\n Magnetic resonance imaging, 73. November 2020.\n Publisher: Magn Reson Imaging\n\n\n\n
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\n\n\n@article{r_quantitative_2020,\n\ttitle = {Quantitative {T2} mapping using accelerated {3D} stack-of-spiral gradient echo readout},\n\tvolume = {73},\n\tissn = {1873-5894},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32860871/},\n\tdoi = {10.1016/j.mri.2020.08.007},\n\tabstract = {This work demonstrated the feasibility of a T$_{\\textrm{2}}$ quantification technique with 3D high-resolution and whole-brain coverage in 2-3 min. The proposed iterative reconstruction method, which utilized the model consistency, data consistency and spatial sparsity jointly, provided reasonable T …},\n\tlanguage = {en},\n\turldate = {2023-02-22},\n\tjournal = {Magnetic resonance imaging},\n\tauthor = {R, Zi and D, Zhu and Q, Qin},\n\tmonth = nov,\n\tyear = {2020},\n\tpmid = {32860871},\n\tnote = {Publisher: Magn Reson Imaging},\n}\n\n\n\n This work demonstrated the feasibility of a T$_{\\textrm{2}}$ quantification technique with 3D high-resolution and whole-brain coverage in 2-3 min. The proposed iterative reconstruction method, which utilized the model consistency, data consistency and spatial sparsity jointly, provided reasonable T …\n
\n\n\n\n \n\n \n \n \n \n \n \n Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis.\n \n \n \n \n\n\n \n Vos, S. B.; Winston, G. P.; Goodkin, O.; Pemberton, H. G.; Barkhof, F.; Prados, F.; Galovic, M.; Koepp, M.; Ourselin, S.; Cardoso, M. J.; and Duncan, J. S.\n\n\n \n\n\n\n Epilepsia, 61(2): 297–309. February 2020.\n \n\n\n\n
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\n\n\n@article{vos_hippocampal_2020,\n\ttitle = {Hippocampal profiling: {Localized} magnetic resonance imaging volumetry and {T2} relaxometry for hippocampal sclerosis},\n\tvolume = {61},\n\tissn = {0013-9580},\n\tshorttitle = {Hippocampal profiling},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065164/},\n\tdoi = {10.1111/epi.16416},\n\tabstract = {Objective\nHippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities.\n\nMethods\nT1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls.\n\nResults\nProfiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values.\n\nSignificance\nHippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.},\n\tnumber = {2},\n\turldate = {2023-02-22},\n\tjournal = {Epilepsia},\n\tauthor = {Vos, Sjoerd B. and Winston, Gavin P. and Goodkin, Olivia and Pemberton, Hugh G. and Barkhof, Frederik and Prados, Ferran and Galovic, Marian and Koepp, Matthias and Ourselin, Sebastien and Cardoso, M. Jorge and Duncan, John S.},\n\tmonth = feb,\n\tyear = {2020},\n\tpmid = {31872873},\n\tpmcid = {PMC7065164},\n\tpages = {297--309},\n}\n\n\n\n Objective Hippocampal sclerosis (HS) is the most common cause of drug‐resistant temporal lobe epilepsy, and its accurate detection is important to guide epilepsy surgery. Radiological features of HS include hippocampal volume loss and increased T2 signal, which can both be quantified to help improve detection. In this work, we extend these quantitative methods to generate cross‐sectional area and T2 profiles along the hippocampal long axis to improve the localization of hippocampal abnormalities. Methods T1‐weighted and T2 relaxometry data from 69 HS patients (32 left, 32 right, 5 bilateral) and 111 healthy controls were acquired on a 3‐T magnetic resonance imaging (MRI) scanner. Automated hippocampal segmentation and T2 relaxometry were performed and used to calculate whole‐hippocampal volumes and to estimate quantitative T2 (qT2) values. By generating a group template from the controls, and aligning this so that the hippocampal long axes were along the anterior‐posterior axis, we were able to calculate hippocampal cross‐sectional area and qT2 by a slicewise method to localize any volume loss or T2 hyperintensity. Individual patient profiles were compared with normative data generated from the healthy controls. Results Profiling of hippocampal volumetric and qT2 data could be performed automatically and reproducibly. HS patients commonly showed widespread decreases in volume and increases in T2 along the length of the affected hippocampus, and focal changes may also be identified. Patterns of atrophy and T2 increase in the left hippocampus were similar between left, right, and bilateral HS. These profiles have potential to distinguish between sclerosis affecting volume and qT2 in the whole or parts of the hippocampus, and may aid the radiological diagnosis in uncertain cases or cases with subtle or focal abnormalities where standard whole‐hippocampal measurements yield normal values. Significance Hippocampal profiling of volumetry and qT2 values can help spatially localize hippocampal MRI abnormalities and work toward improved sensitivity of subtle focal lesions.\n
\n\n\n\n \n\n \n \n \n \n \n \n Comprehensive Evaluation of B1+-corrected FISP-based Magnetic Resonance Fingerprinting: Accuracy, Repeatability and Reproducibility of T1 and T2 Relaxation Times for ISMRM/NIST System Phantom and Volunteers.\n \n \n \n \n\n\n \n Y, K.; K, I.; K, O.; T, T.; H, K.; K, M.; K, M.; G, K.; J, P.; M, N.; and S, N.\n\n\n \n\n\n\n Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 19(3). August 2020.\n Publisher: Magn Reson Med Sci\n\n\n\n
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\n\n\n@article{y_comprehensive_2020,\n\ttitle = {Comprehensive {Evaluation} of {B1}+-corrected {FISP}-based {Magnetic} {Resonance} {Fingerprinting}: {Accuracy}, {Repeatability} and {Reproducibility} of {T1} and {T2} {Relaxation} {Times} for {ISMRM}/{NIST} {System} {Phantom} and {Volunteers}},\n\tvolume = {19},\n\tissn = {1880-2206},\n\tshorttitle = {Comprehensive {Evaluation} of {B1}+-corrected {FISP}-based {Magnetic} {Resonance} {Fingerprinting}},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/31217366/},\n\tdoi = {10.2463/mrms.mp.2019-0016},\n\tabstract = {B$_{\\textrm{1}}$$^{\\textrm{+}}$-corrected FISP-MRF showed an acceptable accuracy, repeatability and reproducibility in the phantom and volunteer studies.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-02-22},\n\tjournal = {Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine},\n\tauthor = {Y, Kato and K, Ichikawa and K, Okudaira and T, Taoka and H, Kawaguchi and K, Murata and K, Maruyama and G, Koerzdoerfer and J, Pfeuffer and M, Nittka and S, Naganawa},\n\tmonth = aug,\n\tyear = {2020},\n\tpmid = {31217366},\n\tnote = {Publisher: Magn Reson Med Sci},\n}\n\n\n\n B$_{\\textrm{1}}$$^{\\textrm{+}}$-corrected FISP-MRF showed an acceptable accuracy, repeatability and reproducibility in the phantom and volunteer studies.\n
\n\n\n\n \n\n \n \n \n \n \n \n Accelerated T2 Mapping of the Lumbar Intervertebral Disc: Highly Undersampled K-Space Data for Robust T2 Relaxation Time Measurement in Clinically Feasible Acquisition Times.\n \n \n \n \n\n\n \n M, R.; M, S.; T, H.; T, K.; M, W.; R, W.; S, T.; and V, J.\n\n\n \n\n\n\n Investigative radiology, 55(11). November 2020.\n Publisher: Invest Radiol\n\n\n\n
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\n\n\n@article{m_accelerated_2020,\n\ttitle = {Accelerated {T2} {Mapping} of the {Lumbar} {Intervertebral} {Disc}: {Highly} {Undersampled} {K}-{Space} {Data} for {Robust} {T2} {Relaxation} {Time} {Measurement} in {Clinically} {Feasible} {Acquisition} {Times}},\n\tvolume = {55},\n\tissn = {1536-0210},\n\tshorttitle = {Accelerated {T2} {Mapping} of the {Lumbar} {Intervertebral} {Disc}},\n\turl = {https://pubmed.ncbi.nlm.nih.gov/32649331/},\n\tdoi = {10.1097/RLI.0000000000000690},\n\tabstract = {GRAPPATINI facilitates precise T2 mapping at 3 T in accordance with clinical standards and reference methods using the same parameters while shortening acquisition times from 13:18 to 2:27 minutes with the same parameters.},\n\tlanguage = {en},\n\tnumber = {11},\n\turldate = {2023-02-22},\n\tjournal = {Investigative radiology},\n\tauthor = {M, Raudner and M, Schreiner and T, Hilbert and T, Kober and M, Weber and R, Windhager and S, Trattnig and V, Juras},\n\tmonth = nov,\n\tyear = {2020},\n\tpmid = {32649331},\n\tnote = {Publisher: Invest Radiol},\n}\n\n\n GRAPPATINI facilitates precise T2 mapping at 3 T in accordance with clinical standards and reference methods using the same parameters while shortening acquisition times from 13:18 to 2:27 minutes with the same parameters.\n
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\n \n \n\n \n \n \n \n \n Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.\n \n \n \n\n\n \n Keenan, K. E.; Biller, J. R.; Delfino, J. G.; Boss, M. A.; Does, M. D.; Evelhoch, J. L.; Griswold, M. A.; Gunter, J. L.; Hinks, R. S.; Hoffman, S. W.; Kim, G.; Lattanzi, R.; Li, X.; Marinelli, L.; Metzger, G. J.; Mukherjee, P.; Nordstrom, R. J.; Peskin, A. P.; Perez, E.; Russek, S. E.; Sahiner, B.; Serkova, N.; Shukla-Dave, A.; Steckner, M.; Stupic, K. F.; Wilmes, L. J.; Wu, H. H.; Zhang, H.; Jackson, E. F.; and Sullivan, D. C.\n\n\n \n\n\n\n Journal of magnetic resonance imaging: JMRI, 49(7): e26–e39. June 2019.\n \n\n\n\n
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\n\n\n@article{keenan_recommendations_2019,\n\ttitle = {Recommendations towards standards for quantitative {MRI} ({qMRI}) and outstanding needs},\n\tvolume = {49},\n\tissn = {1522-2586},\n\tdoi = {10.1002/jmri.26598},\n\tabstract = {5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.},\n\tlanguage = {eng},\n\tnumber = {7},\n\tjournal = {Journal of magnetic resonance imaging: JMRI},\n\tauthor = {Keenan, Kathryn E. and Biller, Joshua R. and Delfino, Jana G. and Boss, Michael A. and Does, Mark D. and Evelhoch, Jeffrey L. and Griswold, Mark A. and Gunter, Jeffrey L. and Hinks, R. Scott and Hoffman, Stuart W. and Kim, Geena and Lattanzi, Riccardo and Li, Xiaojuan and Marinelli, Luca and Metzger, Gregory J. and Mukherjee, Pratik and Nordstrom, Robert J. and Peskin, Adele P. and Perez, Elena and Russek, Stephen E. and Sahiner, Berkman and Serkova, Natalie and Shukla-Dave, Amita and Steckner, Michael and Stupic, Karl F. and Wilmes, Lisa J. and Wu, Holden H. and Zhang, Huiming and Jackson, Edward F. and Sullivan, Daniel C.},\n\tmonth = jun,\n\tyear = {2019},\n\tpmid = {30680836},\n\tpmcid = {PMC6663309},\n\tkeywords = {Anthropometry, Breast, Decision Making, Deep Learning, Equipment Design, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Phantoms, Imaging, Precision Medicine, Radiology, Interventional, Reference Standards, Reference Values, Reproducibility of Results, Robotics, Software, phantom, quantitative MRI, reference objects, standards, validation},\n\tpages = {e26--e39},\n}\n\n\n\n 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.\n
\n\n\n\n \n\n \n \n \n \n \n \n T1 and T2 quantification from standard turbo spin echo images.\n \n \n \n \n\n\n \n McPhee, K. C.; and Wilman, A. H.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 81(3): 2052–2063. 2019.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27495\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{mcphee_t1_2019,\n\ttitle = {T1 and {T2} quantification from standard turbo spin echo images},\n\tvolume = {81},\n\tcopyright = {© 2018 International Society for Magnetic Resonance in Medicine},\n\tissn = {1522-2594},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.27495},\n\tdoi = {10.1002/mrm.27495},\n\tabstract = {Purpose To extract longitudinal and transverse (T1 and T2) relaxation maps from standard MRI methods. Methods Bloch simulations were used to model relative signal amplitudes from standard turbo spin-echo sequences: proton density weighted, T2-weighted, and either T2-weighted fluid attenuated inversion recovery or T1-weighted images. Simulations over a range of expected parameter values yielded a look-up table of relative signal intensities of these sequences. Weighted images and flip angle maps were acquired in 8 subjects at 3 T using both single and multislice acquisitions. The T1 and T2 maps were fit by comparing the weighted images to the look-up table, given the measured flip angles. Results were compared with inversion recovery and multi-echo spin-echo experiments. Results A region analysis showed that relaxation maps computed from single-slice proton density, T2 and T1 weighting provided a mean T1 error of 4\\% in gray matter and 11\\% in white matter, and a mean T2 error of 3\\% and 4\\%, respectively, in comparison to reference measurements. In multislice acquisitions that are optimized to reduce cross-talk and incidental magnetization transfer, the mean T1 error was 7\\% in gray matter and 1\\% in white matter, and the mean T2 errors were 3\\% and 4\\%, respectively. The best T1 results were achieved using proton density, T2 and T1 weighting rather than the fluid attenuated inversion recovery, although T2 maps were largely unaffected by this choice. Incidental magnetization transfer reduced T1 accuracy in standard interleaved multislice acquisitions. Conclusion Through exact sequence modeling and separate flip angle measurement, T2 and T1 may be quantified from a turbo spin-echo brain protocol with proton density, T2, and T1 weighting.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-09-12},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {McPhee, Kelly C. and Wilman, Alan H.},\n\tyear = {2019},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27495},\n\tkeywords = {T1, T2, relaxometry, turbo spin echo},\n\tpages = {2052--2063},\n}\n\n\n\n Purpose To extract longitudinal and transverse (T1 and T2) relaxation maps from standard MRI methods. Methods Bloch simulations were used to model relative signal amplitudes from standard turbo spin-echo sequences: proton density weighted, T2-weighted, and either T2-weighted fluid attenuated inversion recovery or T1-weighted images. Simulations over a range of expected parameter values yielded a look-up table of relative signal intensities of these sequences. Weighted images and flip angle maps were acquired in 8 subjects at 3 T using both single and multislice acquisitions. The T1 and T2 maps were fit by comparing the weighted images to the look-up table, given the measured flip angles. Results were compared with inversion recovery and multi-echo spin-echo experiments. Results A region analysis showed that relaxation maps computed from single-slice proton density, T2 and T1 weighting provided a mean T1 error of 4% in gray matter and 11% in white matter, and a mean T2 error of 3% and 4%, respectively, in comparison to reference measurements. In multislice acquisitions that are optimized to reduce cross-talk and incidental magnetization transfer, the mean T1 error was 7% in gray matter and 1% in white matter, and the mean T2 errors were 3% and 4%, respectively. The best T1 results were achieved using proton density, T2 and T1 weighting rather than the fluid attenuated inversion recovery, although T2 maps were largely unaffected by this choice. Incidental magnetization transfer reduced T1 accuracy in standard interleaved multislice acquisitions. Conclusion Through exact sequence modeling and separate flip angle measurement, T2 and T1 may be quantified from a turbo spin-echo brain protocol with proton density, T2, and T1 weighting.\n
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\n \n \n\n \n \n \n \n \n \n Let’s talk about cardiac T1 mapping.\n \n \n \n \n\n\n \n Hafyane, T.; Karakuzu, A.; Duquette, C.; Mongeon, F.; Cohen-Adad, J.; Jerosch-Herold, M.; Friedrich, M. G.; and Stikov, N.\n\n\n \n\n\n\n June 2018.\n Pages: 343079 Section: New Results\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@misc{hafyane_lets_2018,\n\ttitle = {Let’s talk about cardiac {T1} mapping},\n\tcopyright = {© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},\n\turl = {https://www.biorxiv.org/content/10.1101/343079v1},\n\tdoi = {10.1101/343079},\n\tabstract = {Background Recent reports have shown that T1 mapping sequences agree in phantoms, but exhibit significant differences in vivo. To characterize these differences in the heart, one needs to consider the effects of magnetization transfer (MT) and the T2 relaxation time in the most commonly used cardiac T1 mapping sequences (MOLLI, ShMOLLI and SASHA).\nMethods Six explanted pig hearts were scanned weekly over a period of six weeks on a 3T system with the MOLLI, ShMOLLI, SASHA sequences and an inversion recovery sequence as reference. The T1 bias was computed as the difference between MOLLI, ShMOLLI, SASHA and the reference T1 values. We applied robust correlation statistics to assess the relationships between T1, T2 and MT. All data are publicly available at: http://neuropoly.pub/pigHeartsData.\nResults A systematic T1 bias was present for all sequences, with MOLLI and ShMOLLI underestimating T1 and SASHA slightly overestimating T1 compared to the reference. The correlation of T1 bias with T2 was weak and insignificant. However, MT showed significant associations with T1 bias for all sequences. Our analysis is also available at: http://neuropoly.pub/pigHeartsInteractive.\nConclusion We investigated cardiac T1 mapping sequences in a setting that allowed us to explore their accuracy and their dependence on T2 and MT effects. The T2 effects were not significant, and could not explain the T1 bias of MOLLI, ShMOLLI, SASHA with respect to the reference. On the other hand, the T1 biases exhibited a strong correlation with MT. We conclude that inaccuracies in cardiac T1 mapping are primarily due to magnetization transfer.},\n\tlanguage = {en},\n\turldate = {2023-06-13},\n\tpublisher = {bioRxiv},\n\tauthor = {Hafyane, Tarik and Karakuzu, Agah and Duquette, Catherine and Mongeon, François-Pierre and Cohen-Adad, Julien and Jerosch-Herold, Michael and Friedrich, Matthias G. and Stikov, Nikola},\n\tmonth = jun,\n\tyear = {2018},\n\tnote = {Pages: 343079\nSection: New Results},\n}\n\n\n\n Background Recent reports have shown that T1 mapping sequences agree in phantoms, but exhibit significant differences in vivo. To characterize these differences in the heart, one needs to consider the effects of magnetization transfer (MT) and the T2 relaxation time in the most commonly used cardiac T1 mapping sequences (MOLLI, ShMOLLI and SASHA). Methods Six explanted pig hearts were scanned weekly over a period of six weeks on a 3T system with the MOLLI, ShMOLLI, SASHA sequences and an inversion recovery sequence as reference. The T1 bias was computed as the difference between MOLLI, ShMOLLI, SASHA and the reference T1 values. We applied robust correlation statistics to assess the relationships between T1, T2 and MT. All data are publicly available at: http://neuropoly.pub/pigHeartsData. Results A systematic T1 bias was present for all sequences, with MOLLI and ShMOLLI underestimating T1 and SASHA slightly overestimating T1 compared to the reference. The correlation of T1 bias with T2 was weak and insignificant. However, MT showed significant associations with T1 bias for all sequences. Our analysis is also available at: http://neuropoly.pub/pigHeartsInteractive. Conclusion We investigated cardiac T1 mapping sequences in a setting that allowed us to explore their accuracy and their dependence on T2 and MT effects. The T2 effects were not significant, and could not explain the T1 bias of MOLLI, ShMOLLI, SASHA with respect to the reference. On the other hand, the T1 biases exhibited a strong correlation with MT. We conclude that inaccuracies in cardiac T1 mapping are primarily due to magnetization transfer.\n
\n\n\n\n \n\n \n \n \n \n \n \n Inferring brain tissue composition and microstructure via MR relaxometry.\n \n \n \n \n\n\n \n Does, M. D.\n\n\n \n\n\n\n NeuroImage, 182: 136–148. November 2018.\n \n\n\n\n
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\n\n\n@article{does_inferring_2018,\n\tseries = {Microstructural {Imaging}},\n\ttitle = {Inferring brain tissue composition and microstructure via {MR} relaxometry},\n\tvolume = {182},\n\tissn = {1053-8119},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1053811917311114},\n\tdoi = {10.1016/j.neuroimage.2017.12.087},\n\tabstract = {MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T1, T2, and T2*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.},\n\turldate = {2023-10-13},\n\tjournal = {NeuroImage},\n\tauthor = {Does, Mark D.},\n\tmonth = nov,\n\tyear = {2018},\n\tkeywords = {MRI, Microstructure, Myelin, Relaxometry},\n\tpages = {136--148},\n}\n\n\n\n MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T1, T2, and T2*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.\n
\n\n\n\n \n\n \n \n \n \n \n \n Let’s talk about cardiac T1 mapping.\n \n \n \n \n\n\n \n Hafyane, T.; Karakuzu, A.; Duquette, C.; Mongeon, F.; Cohen-Adad, J.; Jerosch-Herold, M.; Friedrich, M. G.; and Stikov, N.\n\n\n \n\n\n\n June 2018.\n Pages: 343079 Section: New Results\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@misc{hafyane_lets_2018-1,\n\ttitle = {Let’s talk about cardiac {T1} mapping},\n\tcopyright = {© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},\n\turl = {https://www.biorxiv.org/content/10.1101/343079v1},\n\tdoi = {10.1101/343079},\n\tabstract = {Background Recent reports have shown that T1 mapping sequences agree in phantoms, but exhibit significant differences in vivo. To characterize these differences in the heart, one needs to consider the effects of magnetization transfer (MT) and the T2 relaxation time in the most commonly used cardiac T1 mapping sequences (MOLLI, ShMOLLI and SASHA).\nMethods Six explanted pig hearts were scanned weekly over a period of six weeks on a 3T system with the MOLLI, ShMOLLI, SASHA sequences and an inversion recovery sequence as reference. The T1 bias was computed as the difference between MOLLI, ShMOLLI, SASHA and the reference T1 values. We applied robust correlation statistics to assess the relationships between T1, T2 and MT. All data are publicly available at: http://neuropoly.pub/pigHeartsData.\nResults A systematic T1 bias was present for all sequences, with MOLLI and ShMOLLI underestimating T1 and SASHA slightly overestimating T1 compared to the reference. The correlation of T1 bias with T2 was weak and insignificant. However, MT showed significant associations with T1 bias for all sequences. Our analysis is also available at: http://neuropoly.pub/pigHeartsInteractive.\nConclusion We investigated cardiac T1 mapping sequences in a setting that allowed us to explore their accuracy and their dependence on T2 and MT effects. The T2 effects were not significant, and could not explain the T1 bias of MOLLI, ShMOLLI, SASHA with respect to the reference. On the other hand, the T1 biases exhibited a strong correlation with MT. We conclude that inaccuracies in cardiac T1 mapping are primarily due to magnetization transfer.},\n\tlanguage = {en},\n\turldate = {2023-06-13},\n\tpublisher = {bioRxiv},\n\tauthor = {Hafyane, Tarik and Karakuzu, Agah and Duquette, Catherine and Mongeon, François-Pierre and Cohen-Adad, Julien and Jerosch-Herold, Michael and Friedrich, Matthias G. and Stikov, Nikola},\n\tmonth = jun,\n\tyear = {2018},\n\tnote = {Pages: 343079\nSection: New Results},\n}\n\n\n\n Background Recent reports have shown that T1 mapping sequences agree in phantoms, but exhibit significant differences in vivo. To characterize these differences in the heart, one needs to consider the effects of magnetization transfer (MT) and the T2 relaxation time in the most commonly used cardiac T1 mapping sequences (MOLLI, ShMOLLI and SASHA). Methods Six explanted pig hearts were scanned weekly over a period of six weeks on a 3T system with the MOLLI, ShMOLLI, SASHA sequences and an inversion recovery sequence as reference. The T1 bias was computed as the difference between MOLLI, ShMOLLI, SASHA and the reference T1 values. We applied robust correlation statistics to assess the relationships between T1, T2 and MT. All data are publicly available at: http://neuropoly.pub/pigHeartsData. Results A systematic T1 bias was present for all sequences, with MOLLI and ShMOLLI underestimating T1 and SASHA slightly overestimating T1 compared to the reference. The correlation of T1 bias with T2 was weak and insignificant. However, MT showed significant associations with T1 bias for all sequences. Our analysis is also available at: http://neuropoly.pub/pigHeartsInteractive. Conclusion We investigated cardiac T1 mapping sequences in a setting that allowed us to explore their accuracy and their dependence on T2 and MT effects. The T2 effects were not significant, and could not explain the T1 bias of MOLLI, ShMOLLI, SASHA with respect to the reference. On the other hand, the T1 biases exhibited a strong correlation with MT. We conclude that inaccuracies in cardiac T1 mapping are primarily due to magnetization transfer.\n
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\n\n \n \n 2017\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Estimating MRF Time Point Importance by Random Forest Classification.\n \n \n \n \n\n\n \n Allen, J.; Kennedy, J.; and Jezzard, P.\n\n\n \n\n\n\n In ISMRM Workshop on Magnetic Resonance Fingerprinting, 2017. \n \n\n\n\n
\n\n\n\n \n \n![\"Estimating](\"//bibbase.org/img/filetypes/pdf.svg\"\n\t "\\"Paper\\"\n\t")
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\n\n\n\n@inproceedings{allen_estimating_2017,\n\ttitle = {Estimating {MRF} {Time} {Point} {Importance} by {Random} {Forest} {Classification}},\n\turl = {https://cds.ismrm.org/protected/Fingerprinting17/program/abstracts/Allen.pdf},\n\tbooktitle = {{ISMRM} {Workshop} on {Magnetic} {Resonance} {Fingerprinting}},\n\tauthor = {Allen, Jack and Kennedy, James and Jezzard, Peter},\n\tyear = {2017},\n}\n\n\n\n
\n\n \n \n 2016\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Accelerated and motion-robust in vivo T2 mapping from radially undersampled data using bloch-simulation-based iterative reconstruction.\n \n \n \n \n\n\n \n Ben-Eliezer, N.; Sodickson, D. K.; Shepherd, T.; Wiggins, G. C.; and Block, K. T.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 75(3): 1346–1354. 2016.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.25558\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{ben-eliezer_accelerated_2016,\n\ttitle = {Accelerated and motion-robust in vivo {T2} mapping from radially undersampled data using bloch-simulation-based iterative reconstruction},\n\tvolume = {75},\n\tcopyright = {© 2015 Wiley Periodicals, Inc.},\n\tissn = {1522-2594},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.25558},\n\tdoi = {10.1002/mrm.25558},\n\tabstract = {Purpose Development of a quantitative transverse relaxation time (T2)-mapping platform that operates at clinically feasible timescales by employing advanced image reconstruction of radially undersampled multi spin-echo (MSE) datasets. Methods Data was acquired on phantom and in vivo at 3 Tesla using MSE protocols employing radial k-space sampling trajectories. In order to overcome the nontrivial spin evolution associated with MSE protocols, a numerical signal model was precalculated based on Bloch simulations of the actual pulse-sequence scheme used in the acquisition process. This signal model was subsequently incorporated into an iterative model-based image reconstruction process, producing T2 and proton-density maps. Results T2 maps of phantom and in vivo brain were successfully constructed, closely matching values produced by a single spin-echo reference scan. High-resolution mapping was also performed for the spinal cord in vivo, differentiating the underlying gray/white matter morphology. Conclusion The presented MSE data-processing framework offers reliable mapping of T2 relaxation values in a ∼5-minute timescale, free of user- and scanner-dependent variations. The use of radial k-space sampling provides further advantages in the form of high immunity to irregular physiological motion, as well as enhanced spatial resolutions, owing to its inherent ability to perform alias-free limited field-of-view imaging. Magn Reson Med 75:1346–1354, 2016. © 2015 Wiley Periodicals, Inc.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-09-12},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Ben-Eliezer, Noam and Sodickson, Daniel K. and Shepherd, Timothy and Wiggins, Graham C. and Block, Kai Tobias},\n\tyear = {2016},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.25558},\n\tkeywords = {T2 mapping, model-based reconstruction, quantitative MRI, radial k-space sampling},\n\tpages = {1346--1354},\n}\n\n\n\n Purpose Development of a quantitative transverse relaxation time (T2)-mapping platform that operates at clinically feasible timescales by employing advanced image reconstruction of radially undersampled multi spin-echo (MSE) datasets. Methods Data was acquired on phantom and in vivo at 3 Tesla using MSE protocols employing radial k-space sampling trajectories. In order to overcome the nontrivial spin evolution associated with MSE protocols, a numerical signal model was precalculated based on Bloch simulations of the actual pulse-sequence scheme used in the acquisition process. This signal model was subsequently incorporated into an iterative model-based image reconstruction process, producing T2 and proton-density maps. Results T2 maps of phantom and in vivo brain were successfully constructed, closely matching values produced by a single spin-echo reference scan. High-resolution mapping was also performed for the spinal cord in vivo, differentiating the underlying gray/white matter morphology. Conclusion The presented MSE data-processing framework offers reliable mapping of T2 relaxation values in a ∼5-minute timescale, free of user- and scanner-dependent variations. The use of radial k-space sampling provides further advantages in the form of high immunity to irregular physiological motion, as well as enhanced spatial resolutions, owing to its inherent ability to perform alias-free limited field-of-view imaging. Magn Reson Med 75:1346–1354, 2016. © 2015 Wiley Periodicals, Inc.\n
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\n\n \n \n 2015\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Rapid and accurate T2 mapping from multi–spin-echo data using Bloch-simulation-based reconstruction.\n \n \n \n \n\n\n \n Ben-Eliezer, N.; Sodickson, D. K.; and Block, K. T.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 73(2): 809–817. 2015.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.25156\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{ben-eliezer_rapid_2015,\n\ttitle = {Rapid and accurate {T2} mapping from multi–spin-echo data using {Bloch}-simulation-based reconstruction},\n\tvolume = {73},\n\tcopyright = {© 2014 Wiley Periodicals, Inc.},\n\tissn = {1522-2594},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.25156},\n\tdoi = {10.1002/mrm.25156},\n\tabstract = {Purpose Quantitative T2-relaxation-based contrast has the potential to provide valuable clinical information. Practical T2-mapping, however, is impaired either by prohibitively long acquisition times or by contamination of fast multiecho protocols by stimulated and indirect echoes. This work presents a novel postprocessing approach aiming to overcome the common penalties associated with multiecho protocols, and enabling rapid and accurate mapping of T2 relaxation values. Methods Bloch simulations are used to estimate the actual echo-modulation curve (EMC) in a multi–spin-echo experiment. Simulations are repeated for a range of T2 values and transmit field scales, yielding a database of simulated EMCs, which is then used to identify the T2 value whose EMC most closely matches the experimentally measured data at each voxel. Results T2 maps of both phantom and in vivo scans were successfully reconstructed, closely matching maps produced from single spin-echo data. Results were consistent over the physiological range of T2 values and across different experimental settings. Conclusion The proposed technique allows accurate T2 mapping in clinically feasible scan times, free of user- and scanner-dependent variations, while providing a comprehensive framework that can be extended to model other parameters (e.g., T1, B1+, B0, diffusion) and support arbitrary acquisition schemes. Magn Reson Med 73:809–817, 2015. © 2014 Wiley Periodicals, Inc.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-10-12},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Ben-Eliezer, Noam and Sodickson, Daniel K. and Block, Kai Tobias},\n\tyear = {2015},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.25156},\n\tkeywords = {T2 mapping, quantitative MRI},\n\tpages = {809--817},\n}\n\n\n\n Purpose Quantitative T2-relaxation-based contrast has the potential to provide valuable clinical information. Practical T2-mapping, however, is impaired either by prohibitively long acquisition times or by contamination of fast multiecho protocols by stimulated and indirect echoes. This work presents a novel postprocessing approach aiming to overcome the common penalties associated with multiecho protocols, and enabling rapid and accurate mapping of T2 relaxation values. Methods Bloch simulations are used to estimate the actual echo-modulation curve (EMC) in a multi–spin-echo experiment. Simulations are repeated for a range of T2 values and transmit field scales, yielding a database of simulated EMCs, which is then used to identify the T2 value whose EMC most closely matches the experimentally measured data at each voxel. Results T2 maps of both phantom and in vivo scans were successfully reconstructed, closely matching maps produced from single spin-echo data. Results were consistent over the physiological range of T2 values and across different experimental settings. Conclusion The proposed technique allows accurate T2 mapping in clinically feasible scan times, free of user- and scanner-dependent variations, while providing a comprehensive framework that can be extended to model other parameters (e.g., T1, B1+, B0, diffusion) and support arbitrary acquisition schemes. Magn Reson Med 73:809–817, 2015. © 2014 Wiley Periodicals, Inc.\n
\n\n\n\n \n\n \n \n \n \n \n Fast group matching for MR fingerprinting reconstruction.\n \n \n \n\n\n \n Cauley, S. F.; Setsompop, K.; Ma, D.; Jiang, Y.; Ye, H.; Adalsteinsson, E.; Griswold, M. A.; and Wald, L. L.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 74(2): 523–528. August 2015.\n \n\n\n\n
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\n\n\n\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{cauley_fast_2015,\n\ttitle = {Fast group matching for {MR} fingerprinting reconstruction},\n\tvolume = {74},\n\tissn = {1522-2594},\n\tdoi = {10.1002/mrm.25439},\n\tabstract = {PURPOSE: MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1 , T2 , proton density, and B0 , the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction.\nTHEORY AND METHODS: We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach.\nRESULTS: For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1-2\\% relative error).\nCONCLUSION: The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources.},\n\tlanguage = {eng},\n\tnumber = {2},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Cauley, Stephen F. and Setsompop, Kawin and Ma, Dan and Jiang, Yun and Ye, Huihui and Adalsteinsson, Elfar and Griswold, Mark A. and Wald, Lawrence L.},\n\tmonth = aug,\n\tyear = {2015},\n\tpmid = {25168690},\n\tpmcid = {PMC4700821},\n\tkeywords = {Algorithms, Brain, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, MR fingerprinting, Magnetic Resonance Imaging, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, clustering, matching, pruning},\n\tpages = {523--528},\n}\n\n\n\n PURPOSE: MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1 , T2 , proton density, and B0 , the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction. THEORY AND METHODS: We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach. RESULTS: For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1-2% relative error). CONCLUSION: The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources.\n
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\n\n \n \n 2010\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Quantitative Relaxometry of the Brain.\n \n \n \n \n\n\n \n Deoni, S. C. L.\n\n\n \n\n\n\n Topics in Magnetic Resonance Imaging, 21(2): 101. April 2010.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{deoni_quantitative_2010,\n\ttitle = {Quantitative {Relaxometry} of the {Brain}},\n\tvolume = {21},\n\tissn = {1536-1004},\n\turl = {https://journals.lww.com/topicsinmri/fulltext/2010/04000/quantitative_relaxometry_of_the_brain.5.aspx},\n\tdoi = {10.1097/RMR.0b013e31821e56d8},\n\tabstract = {The exquisite soft tissue contrast provided by magnetic resonance imaging arises principally from differences in the intrinsic relaxation properties, T1 and T2. Although the intricate relationships that link tissue microstructure and the longitudinal and transverse relaxation times remain to be firmly established, quantitative measurement of these parameters, also referred to as quantitative relaxometry, can be informative of disease-related tissue change, developmental plasticity, and other biological processes. Further, relaxometry studies potentially offer a more detailed characterization of tissue, compared with conventional qualitative or weighted imaging approaches.\n The purposes of this review were to briefly review the biophysical basis of relaxation, focusing specifically on the T1, T2, and T2* relaxation times, and to detail some of the more widely used and clinically feasible techniques for their in vivo measurement. We will focus on neuroimaging applications, although the methods described are equally well suited to cardiac, abdominal, and musculoskeletal imaging. Potential sources of error, and methods for their correction, are also touched on. Finally, the combination of relaxation time data with other complementary quantitative imaging data, including diffusion tensor imaging, is discussed, with the aim of more thoroughly characterizing brain tissue.},\n\tlanguage = {en-US},\n\tnumber = {2},\n\turldate = {2024-03-03},\n\tjournal = {Topics in Magnetic Resonance Imaging},\n\tauthor = {Deoni, Sean C. L.},\n\tmonth = apr,\n\tyear = {2010},\n\tpages = {101},\n}\n\n\n\n The exquisite soft tissue contrast provided by magnetic resonance imaging arises principally from differences in the intrinsic relaxation properties, T1 and T2. Although the intricate relationships that link tissue microstructure and the longitudinal and transverse relaxation times remain to be firmly established, quantitative measurement of these parameters, also referred to as quantitative relaxometry, can be informative of disease-related tissue change, developmental plasticity, and other biological processes. Further, relaxometry studies potentially offer a more detailed characterization of tissue, compared with conventional qualitative or weighted imaging approaches. The purposes of this review were to briefly review the biophysical basis of relaxation, focusing specifically on the T1, T2, and T2* relaxation times, and to detail some of the more widely used and clinically feasible techniques for their in vivo measurement. We will focus on neuroimaging applications, although the methods described are equally well suited to cardiac, abdominal, and musculoskeletal imaging. Potential sources of error, and methods for their correction, are also touched on. Finally, the combination of relaxation time data with other complementary quantitative imaging data, including diffusion tensor imaging, is discussed, with the aim of more thoroughly characterizing brain tissue.\n
\n\n\n\n \n\n \n \n \n \n \n \n Quantitative T2 analysis: The effects of noise, regularization, and multivoxel approaches.\n \n \n \n \n\n\n \n Bjarnason, T. A.; McCreary, C. R.; Dunn, J. F.; and Mitchell, J. R.\n\n\n \n\n\n\n Magnetic Resonance in Medicine, 63(1): 212–217. 2010.\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22173\n\n\n\n
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\n\n\n@article{bjarnason_quantitative_2010,\n\ttitle = {Quantitative {T2} analysis: {The} effects of noise, regularization, and multivoxel approaches},\n\tvolume = {63},\n\tcopyright = {Copyright © 2009 Wiley-Liss, Inc.},\n\tissn = {1522-2594},\n\tshorttitle = {Quantitative {T2} analysis},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.22173},\n\tdoi = {10.1002/mrm.22173},\n\tabstract = {Typical quantitative T2 (qT2) analysis involves creating T2 distributions using a regularized algorithm from region-of-interest averaged decay data. This study uses qT2 analysis of simulated and experimental decay signals to determine how (a) noise-type, (b) regularization, and (c) region-of-interest versus multivoxel analyses affect T2 distributions. Our simulations indicate that regularization causes myelin water fraction and intra/extracellular water geometric mean T2 underestimation that worsens as the signal-to-noise ratio decreases. The underestimation was greater for intra/extracellular water geometric mean T2 measures using Rician noise. Simulations showed significant differences between myelin water fractions determined using region-of-interest and multivoxel approaches compared to the true value. The nonregularized voxel-based approach gave the most accurate measure of myelin water fraction and intra/extracellular water geometric mean T2 for a given signal-to-noise ratio and noise type. Additionally, multivoxel analysis provides important information about the variability of the analysis. Results obtained from in vivo rat data were similar to our simulation results. In each case, a nonregularized, multivoxel analysis provided myelin water fractions significantly different from the regularized approaches and obtained the largest myelin water fraction. We conclude that quantitative T2 analysis is best performed using a nonregularized, multivoxel approach. Magn Reson Med, 2010. © 2009 Wiley-Liss, Inc.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-09-12},\n\tjournal = {Magnetic Resonance in Medicine},\n\tauthor = {Bjarnason, Thorarin A. and McCreary, Cheryl R. and Dunn, Jeff F. and Mitchell, J. Ross},\n\tyear = {2010},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.22173},\n\tkeywords = {T2 distribution, T2 relaxation, rat, voxel-based analysis, white matter},\n\tpages = {212--217},\n}\n\n\n\n Typical quantitative T2 (qT2) analysis involves creating T2 distributions using a regularized algorithm from region-of-interest averaged decay data. This study uses qT2 analysis of simulated and experimental decay signals to determine how (a) noise-type, (b) regularization, and (c) region-of-interest versus multivoxel analyses affect T2 distributions. Our simulations indicate that regularization causes myelin water fraction and intra/extracellular water geometric mean T2 underestimation that worsens as the signal-to-noise ratio decreases. The underestimation was greater for intra/extracellular water geometric mean T2 measures using Rician noise. Simulations showed significant differences between myelin water fractions determined using region-of-interest and multivoxel approaches compared to the true value. The nonregularized voxel-based approach gave the most accurate measure of myelin water fraction and intra/extracellular water geometric mean T2 for a given signal-to-noise ratio and noise type. Additionally, multivoxel analysis provides important information about the variability of the analysis. Results obtained from in vivo rat data were similar to our simulation results. In each case, a nonregularized, multivoxel analysis provided myelin water fractions significantly different from the regularized approaches and obtained the largest myelin water fraction. We conclude that quantitative T2 analysis is best performed using a nonregularized, multivoxel approach. Magn Reson Med, 2010. © 2009 Wiley-Liss, Inc.\n
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\n \n \n\n \n \n \n \n \n Towards the Perfect Quantitative MRI machine.\n \n \n \n\n\n \n Tofts, P. S; Dowell, N. G; and Cercignani, M.\n\n\n \n\n\n\n . .\n \n\n\n\n
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\n\n\n\n@article{tofts_towards_nodate,\n\ttitle = {Towards the {Perfect} {Quantitative} {MRI} machine},\n\tlanguage = {en},\n\tauthor = {Tofts, Paul S and Dowell, Nicholas G and Cercignani, Mara},\n}\n\n\n\n \n\n \n \n \n \n \n \n Steps on the Path to Clinical Translation: A workshop by the British and Irish Chapter of the ISMRM - Hubbard Cristinacce - Magnetic Resonance in Medicine - Wiley Online Library.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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\n\n\n\n@misc{noauthor_steps_nodate,\n\ttitle = {Steps on the {Path} to {Clinical} {Translation}: {A} workshop by the {British} and {Irish} {Chapter} of the {ISMRM} - {Hubbard} {Cristinacce} - {Magnetic} {Resonance} in {Medicine} - {Wiley} {Online} {Library}},\n\turl = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.29704},\n\turldate = {2023-05-30},\n}\n\n\n\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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\n\n\n\n\n \n\n \n \n \n \n \n \n ISMRM21 - MRF & Synthetic MR: What Is It All About & When Can I Start Using It Clinically?.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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\n\n\n\n@misc{noauthor_ismrm21_nodate,\n\ttitle = {{ISMRM21} - {MRF} \\& {Synthetic} {MR}: {What} {Is} {It} {All} {About} \\& {When} {Can} {I} {Start} {Using} {It} {Clinically}?},\n\turl = {https://www.ismrm.org/21/program-files/S-04a.htm},\n\turldate = {2023-06-13},\n}\n\n\n\n \n\n \n \n \n \n \n .\n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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\n\n\n\n\n \n\n \n \n \n \n \n \n High-resolution diffusion tensor imaging and T2 mapping detect regional changes within the hippocampus in multiple sclerosis.\n \n \n \n \n\n\n \n Valdés Cabrera, D.; Blevins, G.; Smyth, P.; Emery, D.; Solar, K. G.; and Beaulieu, C.\n\n\n \n\n\n\n NMR in Biomedicine, n/a(n/a): e4952. .\n _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.4952\n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{valdes_cabrera_high-resolution_nodate,\n\ttitle = {High-resolution diffusion tensor imaging and {T2} mapping detect regional changes within the hippocampus in multiple sclerosis},\n\tvolume = {n/a},\n\tcopyright = {© 2023 The Authors. NMR in Biomedicine published by John Wiley \\& Sons Ltd.},\n\tissn = {1099-1492},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.4952},\n\tdoi = {10.1002/nbm.4952},\n\tabstract = {Hippocampus demyelinating lesions in multiple sclerosis (MS) have been frequently observed in ex vivo histopathological studies; however, they are difficult to image and quantify in vivo. Diffusion tensor imaging (DTI) and T2 mapping could potentially detect such regional in vivo changes if acquired with sufficient spatial resolution. The goal here was to evaluate whether there are focal hippocampal abnormalities in 43 MS patients (35 relapsing–remitting, eight secondary progressive) with and without cognitive impairment (CI) versus 43 controls using high-resolution 1 mm isotropic DTI, as well as complementary methods of T2-weighted and T2 mapping at 3 T. Abnormal hippocampus regions were identified voxel-by-voxel by using mean diffusivity (MD)/T2 thresholds and avoiding voxels attributed to cerebrospinal fluid. When compared with controls, averaged left/right whole hippocampus MD was higher in both MS groups, while lower fractional anisotropy (FA) and volume, and higher T2 relaxometry and T2-weighted signal values, were only significant in CI MS. The hippocampal MD and T2 images/maps were not uniformly affected and focal regions of elevated MD/T2 were evident in MS patients. Both CI and not CI MS groups showed greater proportional areas of the hippocampus with elevated MD, whereas only the CI group showed a greater proportional area of elevated T2 relaxation times or T2-weighted signal. Higher T2 relaxometry and T2-weighted signal values of elevated regions correlated with greater disability and whole hippocampus FA negatively correlated with physical fatigue. High-resolution hippocampus DTI and T2 mapping with less partial volume effects showed whole hippocampus abnormalities with regional elevations of MD/T2 in MS, which could be interpreted as potentially from demyelination, neuron loss, and/or inflammation, and which overall were more extensive in the hippocampus of patients with larger total brain lesion volumes and CI.},\n\tlanguage = {en},\n\tnumber = {n/a},\n\turldate = {2023-06-13},\n\tjournal = {NMR in Biomedicine},\n\tauthor = {Valdés Cabrera, Diana and Blevins, Gregg and Smyth, Penelope and Emery, Derek and Solar, Kevin Grant and Beaulieu, Christian},\n\tnote = {\\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.4952},\n\tkeywords = {T2 mapping, atrophy, demyelination, diffusion tensor imaging, hippocampus, inflammation, multiple sclerosis},\n\tpages = {e4952},\n}\n\n\n\n Hippocampus demyelinating lesions in multiple sclerosis (MS) have been frequently observed in ex vivo histopathological studies; however, they are difficult to image and quantify in vivo. Diffusion tensor imaging (DTI) and T2 mapping could potentially detect such regional in vivo changes if acquired with sufficient spatial resolution. The goal here was to evaluate whether there are focal hippocampal abnormalities in 43 MS patients (35 relapsing–remitting, eight secondary progressive) with and without cognitive impairment (CI) versus 43 controls using high-resolution 1 mm isotropic DTI, as well as complementary methods of T2-weighted and T2 mapping at 3 T. Abnormal hippocampus regions were identified voxel-by-voxel by using mean diffusivity (MD)/T2 thresholds and avoiding voxels attributed to cerebrospinal fluid. When compared with controls, averaged left/right whole hippocampus MD was higher in both MS groups, while lower fractional anisotropy (FA) and volume, and higher T2 relaxometry and T2-weighted signal values, were only significant in CI MS. The hippocampal MD and T2 images/maps were not uniformly affected and focal regions of elevated MD/T2 were evident in MS patients. Both CI and not CI MS groups showed greater proportional areas of the hippocampus with elevated MD, whereas only the CI group showed a greater proportional area of elevated T2 relaxation times or T2-weighted signal. Higher T2 relaxometry and T2-weighted signal values of elevated regions correlated with greater disability and whole hippocampus FA negatively correlated with physical fatigue. High-resolution hippocampus DTI and T2 mapping with less partial volume effects showed whole hippocampus abnormalities with regional elevations of MD/T2 in MS, which could be interpreted as potentially from demyelination, neuron loss, and/or inflammation, and which overall were more extensive in the hippocampus of patients with larger total brain lesion volumes and CI.\n
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