Review Article
Diffusion Imaging in the Post HCP Era
Steen Moeller PhD,
Pramod Pisharady Kumar PhD,
Jesper Andersson PhD,
Mehmet Akcakaya PhD,
Noam Harel PhD,
Ruoyun(Emily) Ma PhD,
Xiaoping Wu PhD,
Essa Yacoub PhD,
Christophe Lenglet PhD,
Kamil Ugurbil PhD,
Corresponding Author
Steen Moeller PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Address reprint requests to: Steen Moeller, PhD, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN 55455, USA. E-mail: [email protected]Search for more papers by this authorPramod Pisharady Kumar PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorJesper Andersson PhD
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
Search for more papers by this authorMehmet Akcakaya PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorNoam Harel PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorRuoyun(Emily) Ma PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorXiaoping Wu PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorEssa Yacoub PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorChristophe Lenglet PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorKamil Ugurbil PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorSteen Moeller PhD,
Pramod Pisharady Kumar PhD,
Jesper Andersson PhD,
Mehmet Akcakaya PhD,
Noam Harel PhD,
Ruoyun(Emily) Ma PhD,
Xiaoping Wu PhD,
Essa Yacoub PhD,
Christophe Lenglet PhD,
Kamil Ugurbil PhD,
Corresponding Author
Steen Moeller PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Address reprint requests to: Steen Moeller, PhD, Center for Magnetic Resonance Research, University of Minnesota, 2021 6th Street SE, Minneapolis, MN 55455, USA. E-mail: [email protected]Search for more papers by this authorPramod Pisharady Kumar PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorJesper Andersson PhD
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
Search for more papers by this authorMehmet Akcakaya PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorNoam Harel PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorRuoyun(Emily) Ma PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorXiaoping Wu PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorEssa Yacoub PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorChristophe Lenglet PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorKamil Ugurbil PhD
Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
Search for more papers by this authorAbstract
Diffusion imaging is a critical component in the pursuit of developing a better understanding of the human brain. Recent technical advances promise enabling the advancement in the quality of data that can be obtained. In this review the context for different approaches relative to the Human Connectome Project are compared. Significant new gains are anticipated from the use of high-performance head gradients. These gains can be particularly large when the high-performance gradients are employed together with ultrahigh magnetic fields. Transmit array designs are critical in realizing high accelerations in diffusion-weighted (d)MRI acquisitions, while maintaining large field of view (FOV) coverage, and several techniques for optimal signal-encoding are now available. Reconstruction and processing pipelines that precisely disentangle the acquired neuroanatomical information are established and provide the foundation for the application of deep learning in the advancement of dMRI for complex tissues.
Level of Evidence: 3
Technical Efficacy Stage: Stage 3
REFERENCES
- 1Van Essen DC, Smith SM, Barch DM, et al. The WU-Minn human Connectome Project: An overview. Neuroimage 2013; 80: 62-79.
- 2Vu AT, Auerbach E, Lenglet C, et al. High resolution whole brain diffusion imaging at 7T for the Human Connectome Project. Neuroimage 2015; 122: 318-331.
- 3Vu AT, Jamison K, Glasser MF, et al. Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project. Neuroimage 2017; 154: 23-32.
- 4Van Essen DC, Ugurbil K, Auerbach E, et al. The Human Connectome Project: A data acquisition perspective. Neuroimage 2012; 62(4): 2222-2231.
- 5Smith SM, Vidaurre D, Beckmann CF, et al. Functional connectomics from resting-state fMRI. Trends Cogn Sci 2013; 17(12): 666-682.
- 6Sotiropoulos SN, Jbabdi S, Xu J, et al. Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 2013; 80: 125-143.
- 7Ugurbil K, Xu J, Auerbach EJ, et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. Neuroimage 2013; 80: 80-104.
- 8Glasser MF, Smith SM, Marcus DS, et al. The human Connectome Project's neuroimaging approach. Nat Neurosci 2016; 19(9): 1175-1187.
- 9Setsompop K, Kimmlingen R, Eberlein E, et al. Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage 2013; 80: 220-233.
- 10Van Essen DC, Ugurbil K. The future of the human connectome. Neuroimage 2012; 62(2): 1299-1310.
- 11Howell BR, Styner MA, Gao W, et al. The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. Neuroimage 2019; 185: 891-905.
- 12Harms MP, Somerville LH, Ances BM, et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan development and aging projects. Neuroimage 2018; 183: 972-984.
- 13Bookheimer SY, Salat DH, Terpstra M, et al. The Lifespan Human Connectome Project in aging: An overview. Neuroimage 2019; 185: 335-348.
- 14Church GM. BRAIN: Innovative neurotechnologies for imaging and therapeutics. Dialogues Clin Neurosci 2013; 15(3): 241-243.
- 15Leemans A. Diffusion MRI of the brain: The naked truth. NMR Biomed 2019; 32(4):e4084.
- 16Maier-Hein KH, Neher PF, Houde JC, et al. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 2017; 8(1): 1349.
- 17Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016; 125: 1063-1078.
- 18Andersson JL, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. Neuroimage 2003; 20(2): 870-888.
- 19Holdsworth SJ, O'Halloran R, Setsompop K. The quest for high spatial resolution diffusion-weighted imaging of the human brain in vivo. NMR Biomed 2019; 32(4):e4056.
- 20Engstrom M, Martensson M, Avventi E, Skare S. On the signal-to-noise ratio efficiency and slab-banding artifacts in three-dimensional multislab diffusion-weighted echo-planar imaging. Magn Reson Med 2015; 73(2): 718-725.
- 21Alexander DC, Dyrby TB, Nilsson M, Zhang H. Imaging brain microstructure with diffusion MRI: Practicality and applications. NMR Biomed 2019; 32(4):e3841.
- 22Engstrom M, Skare S. Diffusion-weighted 3D multislab echo planar imaging for high signal-to-noise ratio efficiency and isotropic image resolution. Magn Reson Med 2013; 70(6): 1507-1514.
- 23Rooney WD, Johnson G, Li X, et al. Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo. Magn Reson Med 2007; 57(2): 308-318.
- 24Wu W, Poser BA, Douaud G, et al. High-resolution diffusion MRI at 7T using a three-dimensional multislab acquisition. Neuroimage 2016; 143: 1-14.
- 25Bruce IP, Chang HC, Petty C, Chen NK, Song AW. 3D-MB-MUSE: A robust 3D multislab, multi-band and multishot reconstruction approach for ultrahigh resolution diffusion MRI. Neuroimage 2017; 159: 46-56.
- 26Holtrop JL, Sutton BP. High spatial resolution diffusion weighted imaging on clinical 3 T MRI scanners using multislab spiral acquisitions. J Med Imaging 2016; 3(2):023501.
- 27Eichner C, Setsompop K, Koopmans PJ, et al. Slice accelerated diffusion-weighted imaging at ultra-high field strength. Magn Reson Med 2014; 71(4): 1518-1525.
- 28Strotmann B, Heidemann RM, Anwander A, et al. High-resolution MRI and diffusion-weighted imaging of the human habenula at 7 Tesla. J Magn Reson Imaging 2014; 39(4): 1018-1026.
- 29Chang HC, Sundman M, Petit L, et al. Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3Tesla clinical MRI scanner. Neuroimage 2015; 118: 667-675.
- 30Jones DK, Alexander DC, Bowtell R, et al. Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI. Neuroimage 2018; 182: 8-38.
- 31Weiger M, Overweg J, Rosler MB, et al. A high-performance gradient insert for rapid and short-T2 imaging at full duty cycle. Magn Reson Med 2018; 79(6): 3256-3266.
- 32Alsop DC, Connick TJ. Optimization of torque-balanced asymmetric head gradient coils. Magn Reson Med 1996; 35(6): 875-886.
- 33Chronik BA, Alejski A, Rutt BK. Design and fabrication of a three-axis edge ROU head and neck gradient coil. Magn Reson Med 2000; 44(6): 955-963.
- 34Foo TKF, Laskaris E, Vermilyea M, et al. Lightweight, compact, and high-performance 3T MR system for imaging the brain and extremities. Magn Reson Med 2018; 80(5): 2232-2245.
- 35Davids M, Guerin B, Malzacher M, Schad LR, Wald LL. Predicting magnetostimulation thresholds in the peripheral nervous system using realistic body models. Sci Rep 2017; 7(1): 5316.
- 36Wilm BJ, Pruessmann KP, Bertram W, Manuela R, Franciszek H, Markus W, Klaas P, Diffusion MRI of the brain with a gradient strength of 100 mT/m and a slew rate of 1200 T/m/s. In: Proc 27th Annual Meeting ISMRM, Montreal; 2019.
- 37Choi S, Cunningham DT, Aguila F, et al. DTI at 7 and 3 T: Systematic comparison of SNR and its influence on quantitative metrics. Magn Reson Imaging 2011; 29(6): 739-751.
- 38Wu X, Auerbach EJ, Vu AT, et al. High-resolution whole-brain diffusion MRI at 7T using radiofrequency parallel transmission. Magn Reson Med 2018; 80(5): 1857-1870.
- 39Reese TG, Heid O, Weisskoff RM, Wedeen VJ. Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn Reson Med 2003; 49(1): 177-182.
- 40Wilm BJ, Barmet C, Gross S, et al. Single-shot spiral imaging enabled by an expanded encoding model: Demonstration in diffusion MRI. Magn Reson Med 2017; 77(1): 83-91.
- 41Wilm BJ, Barmet C, Pavan M, Pruessmann KP. Higher order reconstruction for MRI in the presence of spatiotemporal field perturbations. Magn Reson Med 2011; 65(6): 1690-1701.
- 42Sotiropoulos SN, Hernandez-Fernandez M, Vu AT, et al. Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project. Neuroimage 2016; 134: 396-409.
- 43Gulban OF, De Martino F, Vu AT, Yacoub E, Ugurbil K, Lenglet C. Cortical fibers orientation mapping using in-vivo whole brain 7T diffusion MRI. Neuroimage 2018; 178: 104-118.
- 44Keil B, Blau JN, Biber S, et al. A 64-channel 3T array coil for accelerated brain MRI. Magn Reson Med 2013; 70(1): 248-258.
- 45Vaidya MV, Sodickson DK, Lattanzi R. Approaching ultimate intrinsic SNR in a uniform spherical sample with finite arrays of loop coils. Concepts Magn Reson Part B Magn Reson Eng 2014; 44(3): 53-65.
- 46Wiggins GC, Polimeni JR, Potthast A, Schmitt M, Alagappan V, Wald LL. 96-channel receive-only head coil for 3 Tesla: Design optimization and evaluation. Magn Reson Med 2009; 62(3): 754-762.
- 47Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med 1999; 42(5): 952-962.
10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S CAS PubMed Web of Science® Google Scholar
- 48Ugurbil K, Auerbach E, Moeller S, et al. Brain imaging with improved acceleration and SNR at 7 Tesla obtained with 64-channel receive array. Magn Reson Med 2019; 82(1): 495-509.
- 49Ugurbil K. Imaging at ultrahigh magnetic fields: History, challenges, and solutions. Neuroimage 2018; 168: 7-32.
- 50De Martino F, Yacoub E, Kemper V, et al. The impact of ultra-high field MRI on cognitive and computational neuroimaging. Neuroimage 2018; 168: 366-382.
- 51Dumoulin SO, Fracasso A, van der Zwaag W, Siero JCW, Petridou N. Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function. Neuroimage 2018; 168: 345-357.
- 52Moeller S, Yacoub E, Olman CA, et al. Multiband multislice GE-EPI at 7 Tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 2010; 63(5): 1144-1153.
- 53Wiesinger F, Van de Moortele PF, Adriany G, De Zanche N, Ugurbil K, Pruessmann KP. Potential and feasibility of parallel MRI at high field. NMR Biomed 2006; 19(3): 368-378.
- 54Ohliger MA, Grant AK, Sodickson DK. Ultimate intrinsic signal-to-noise ratio for parallel MRI: Electromagnetic field considerations. Magn Reson Med 2003; 50(5): 1018-1030.
- 55Barth M, Breuer F, Koopmans PJ, Norris DG, Poser BA. Simultaneous multislice (SMS) imaging techniques. Magn Reson Med 2016; 75(1): 63-81.
- 56Van de Moortele PF, Akgun C, Adriany G, et al. B(1) destructive interferences and spatial phase patterns at 7 T with a head transceiver array coil. Magn Reson Med 2005; 54(6): 1503-1518.
- 57Yang QX, Wang J, Zhang X, et al. Analysis of wave behavior in lossy dielectric samples at high field. Magn Reson Med 2002; 47(5): 982-989.
- 58Guerin B, Setsompop K, Ye H, Poser BA, Stenger AV, Wald LL. Design of parallel transmission pulses for simultaneous multislice with explicit control for peak power and local specific absorption rate. Magn Reson Med 2015; 73(5): 1946-1953.
- 59Sharma A, Bammer R, Stenger VA, Grissom WA. Low peak power multiband spokes pulses for B1 (+) inhomogeneity-compensated simultaneous multislice excitation in high field MRI. Magn Reson Med 2015; 74(3): 747-755.
- 60Wu X, Tian J, Schmitter S, Vaughan JT, Ugurbil K, Van de Moortele PF. Distributing coil elements in three dimensions enhances parallel transmission multiband RF performance: A simulation study in the human brain at 7 Tesla. Magn Reson Med 2016; 75(6): 2464-2472.
- 61Koopmans PJ, Boyacioglu R, Barth M, Norris DG. Whole brain, high resolution spin-echo resting state fMRI using PINS multiplexing at 7 T. Neuroimage 2012; 62(3): 1939-1946.
- 62Wu X, Schmitter S, Auerbach EJ, Moeller S, Ugurbil K, Van de Moortele PF. Simultaneous multislice multiband parallel radiofrequency excitation with independent slice-specific transmit B1 homogenization. Magn Reson Med 2013; 70(3): 630-638.
- 63Poser BA, Anderson RJ, Guerin B, et al. Simultaneous multislice excitation by parallel transmission. Magn Reson Med 2014; 71(4): 1416-1427.
- 64Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 2012; 67(5): 1210-1224.
- 65Zahneisen B, Poser BA, Ernst T, Stenger VA. Three-dimensional Fourier encoding of simultaneously excited slices: Generalized acquisition and reconstruction framework. Magn Reson Med 2014; 71(6): 2071-2081.
- 66Breuer FA, Kannengiesser SA, Blaimer M, Seiberlich N, Jakob PM, Griswold MA. General formulation for quantitative G-factor calculation in GRAPPA reconstructions. Magn Reson Med 2009; 62(3): 739-746.
- 67Xu J, Moeller S, Auerbach EJ, et al. Evaluation of slice accelerations using multiband echo planar imaging at 3 T. Neuroimage 2013; 83: 991-1001.
- 68Cauley SF, Polimeni JR, Bhat H, Wald LL, Setsompop K. Interslice leakage artifact reduction technique for simultaneous multislice acquisitions. Magn Reson Med 2014; 72(1): 93-102.
- 69Todd N, Moeller S, Auerbach EJ, Yacoub E, Flandin G, Weiskopf N. Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts. Neuroimage 2016; 124(Pt A): 32-42.
- 70Moeller S. Simultaneous multislice methods. In: Proc 25th Annual Meeting ISMRM, Honolulu; 2017: 8111.
- 71Liao C, Manhard MK, Bilgic B, et al. Phase-matched virtual coil reconstruction for highly accelerated diffusion echo-planar imaging. Neuroimage 2019; 194: 291-302.
- 72Chieh SW, Kaveh M, Akcakaya M, Moeller S. Self-calibrated interpolation of non-Cartesian data with GRAPPA in parallel imaging. Magn Reson Med 2019; 83 5: 1837-1850.
- 73Knoll F, Hammernik K, Zhang C, et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues. IEEE Sig Process Mag 2020; 37(1): 128-140.
- 74Akcakaya M, Moeller S, Weingartner S, Ugurbil K. Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magn Reson Med 2019; 81(1): 439-453.
- 75Zhang Chi, Hossein Hossini SA, Moeller S, Weingärtner S, Uǧurbil K, Akcakaya M. Scan-Specific Residual Convolutional Neural Networks for Fast MRI Using Residual RAKI. 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA 2019; 1476-1480. https://doi.org/10.1109/IEEECONF44664.2019.9048706.
- 76Burhaneddin Yaman SAHH, Steen Moeller, Jutta Ellermann, Kâmil Ugurbil, Akcakaya Mehmet A. Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data. Magn Reson Med 2020;07669. https://doi.org/10.1002/mrm.28378.
- 77Knoll F, Hammernik K, Zhang C, et al. Deep-learning methods for parallel magnetic resonance imaging reconstruction: a survey of the current approaches. Trends Issues 2020; 37(1): 128-140.
- 78Andersson JL, Sotiropoulos SN. Non-parametric representation and prediction of single- and multishell diffusion-weighted MRI data using Gaussian processes. Neuroimage 2015; 122: 166-176.
- 79Andersson JLR, Graham MS, Drobnjak I, Zhang H, Filippini N, Bastiani M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. Neuroimage 2017; 152: 450-466.
- 80Barmet C, De Zanche N, Pruessmann KP. Spatiotemporal magnetic field monitoring for MR. Magn Reson Med 2008; 60(1): 187-197.
- 81Kasper L, Engel M, Barmet C, et al. Rapid anatomical brain imaging using spiral acquisition and an expanded signal model. Neuroimage 2018; 168: 88-100.
- 82Aranovitch A, Haeberlin M, Gross S, et al. Prospective motion correction with NMR markers using only native sequence elements. Magn Reson Med 2018; 79(4): 2046-2056.
- 83Herbst M, Maclaren J, Weigel M, Korvink J, Hennig J, Zaitsev M. Prospective motion correction with continuous gradient updates in diffusion weighted imaging. Magn Reson Med 2012; 67(2): 326-338.
- 84Behrens TE, Woolrich MW, Jenkinson M, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 2003; 50(5): 1077-1088.
- 85Jbabdi S, Sotiropoulos SN, Savio AM, Grana M, Behrens TE. Model-based analysis of multishell diffusion MR data for tractography: How to get over fitting problems. Magn Reson Med 2012; 68(6): 1846-1855.
- 86Farooq H, Xu J, Nam JW, et al. Microstructure imaging of crossing (MIX) white matter fibers from diffusion MRI. Sci Rep 2016; 6: 38927.
- 87Alexander DC. A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features. Magn Reson Med 2008; 60(2): 439-448.
- 88O'Donnell LJ, Daducci A, Wassermann D, Lenglet C. Advances in computational and statistical diffusion MRI. NMR Biomed 2019; 32(4):e3805.
- 89Sotiropoulos SN, Jbabdi S, Andersson JL, Woolrich MW, Ugurbil K, Behrens TE. RubiX: Combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI. IEEE Trans Med Imaging 2013; 32(6): 969-982.
- 90Pisharady PK, Sotiropoulos SN, Duarte-Carvajalino JM, Sapiro G, Lenglet C. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. Neuroimage 2018; 167: 488-503.
- 91Michailovich O, Rathi Y, Dolui S. Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans Med Imaging 2011; 30(5): 1100-1115.
- 92Ye C, Li X, Chen J. A deep network for tissue microstructure estimation using modified LSTM units. Med Image Anal 2019; 55: 49-64.
- 93Aliotta E, Nourzadeh H, Sanders J, Muller D, Ennis DB. Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks. Med Phys 2019; 46(4): 1581-1591.
- 94Lin Z, Gong T, Wang K, et al. Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network. Med Phys 2019; 46(7): 3101-3116.
- 95Golkov V, Dosovitskiy A, Sperl JI, et al. Q-space deep learning: Twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging 2016; 35(5): 1344-1351.
- 96Zhang Q, Coolen BF, Nederveen AJ, Strijkers GJ. Three-dimensional diffusion imaging with spiral encoded navigators from stimulated echoes (3D-DISPENSE). Magn Reson Med 2019; 81(2): 1052-1065.
- 97Chen NK, Guidon A, Chang HC, Song AW. A robust multishot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). Neuroimage 2013; 72: 41-47.
- 98Truong TK, Guidon A. High-resolution multishot spiral diffusion tensor imaging with inherent correction of motion-induced phase errors. Magn Reson Med 2014; 71(2): 790-796.
- 99Saritas EU, Lee D, Cukur T, Shankaranarayanan A, Nishimura DG. Hadamard slice encoding for reduced-FOV diffusion-weighted imaging. Magn Reson Med 2014; 72(5): 1277-1290.
- 100Setsompop K, Fan Q, Stockmann J, et al. High-resolution in vivo diffusion imaging of the human brain with generalized slice dithered enhanced resolution: Simultaneous multislice (gSlider-SMS). Magn Reson Med 2018; 79(1): 141-151.
- 101Moeller S, Van de Moortele PF, Goerke U, Adriany G, Ugurbil K. Application of parallel imaging to fMRI at 7 Tesla utilizing a high 1D reduction factor. Magn Reson Med 2006; 56(1): 118-129.
- 102Moeller S, Ramanna S, Lenglet C, Pisharady P, Auerbach E, Delabarre L, Wu X, Akcakaya M, Ugurbil K. Self navigation for 3D multishot EPI with data-reference. Magn Reson Med 2020 [epub ahead of print] DOI: https://doi.org/10.1002/mrm.28231.
- 103Glover GH. Spiral imaging in fMRI. Neuroimage 2012; 62(2): 706-712.
- 104Block KT, Frahm J. Spiral imaging: A critical appraisal. J Magn Reson Imaging 2005; 21(6): 657-668.
- 105Robison RK, Li Z, Wang D, Ooi MB, Pipe JG. Correction of B0 eddy current effects in spiral MRI. Magn Reson Med 2019; 81(4): 2501-2513.
- 106Testud F, Gallichan D, Layton KJ, et al. Single-shot imaging with higher-dimensional encoding using magnetic field monitoring and concomitant field correction. Magn Reson Med 2015; 73(3): 1340-1357.
- 107Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage 2016; 142: 394-406.
- 108Vannesjo SJ, Graedel NN, Kasper L, et al. Image reconstruction using a gradient impulse response model for trajectory prediction. Magn Reson Med 2016; 76(1): 45-58.
- 109Duyn JH, Yang Y, Frank JA, van der Veen JW. Simple correction method for k-space trajectory deviations in MRI. J Magn Reson 1998; 132(1): 150-153.
