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Pushing the limits of in vivo diffusion MRI for the Human Connectome Project
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Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with G(max) = 300 mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.
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“The MGH-UCLA HCP is using extremely powerful MRI gradients to generate leading-edge 3T diffusion MRI data.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [16]
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Shi, D., Wang, X., Li, S., Liu, F., Jiang, X., Chen, L., Zhang, J., Guo, H., & Xu, J. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110433 · Cited by 16
Lange, F., Eldebakey, H., Hilgenberg, A., Weigl, B., Eckert, M., DeSunda, A., Neugebauer, H., Peach, R., Roothans, J., Volkmann, J., et al. (2023) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2023.105347 · Cited by 16
Escrichs, A., Perl, Y. S., Fisher, P. M., Martínez‐Molina, N., Guzman, E. G., Frøkjær, V. G., Kringelbach, M. L., Knudsen, G. M., & Deco, G. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02690-7 · Cited by 17
Williamson, N. H., Witherspoon, V. J., Cai, T. X., Ravin, R., Horkay, F., & Basser, P. J. (2023) · Magnetic Resonance Letters · doi:10.1016/j.mrl.2023.03.009 · Cited by 16
An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity
Shamir, I., & Assaf, Y. (2020) · Neuroinformatics · doi:10.1007/s12021-020-09491-7 · Cited by 16
Volumetric and fiber-tracing MRI methods for gray and white matter
Larvie, M., & Fischl, B. R. (2016) · Handbook of clinical neurology · doi:10.1016/b978-0-444-53485-9.00003-9 · Cited by 18
Ramos‐Llordén, G., Park, D. J., KIRSCH, J. E., Scholz, A., Keil, B. R., Maffei, C., Lee, H., Bilgiç, B., Edlow, B. L., Mekkaoui, C., et al. (2023) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29873 · Cited by 15
Self-citationMonti, J. M., Perl, Y. S., Tagliazucchi, E., Kringelbach, M. L., & Deco, G. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013301 · Cited by 15
Wiesmueller, M., Wuest, W., May, M. S., Ellmann, S., Heiss, R., Saake, M., Janka, R., Uder, M., & Laun, F. B. (2021) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7112 · Cited by 15
Circular and unified analysis in network neuroscience
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Bridging the gap between system and cell: The role of ultra-high field MRI in human neuroscience
Turner, R., & Haan, D. D. D. (2017) · Progress in brain research · doi:10.1016/bs.pbr.2017.05.005 · Cited by 15
MRI-based assessment of function and dysfunction in myelinated axons
Spees, W. M., Lin, T., Sun, P., Song, C., George, A., Gary, S. E., Yang, H.-C., & Song, S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801788115 · Cited by 15
Naughton, N. M., Tennyson, C. G., & Georgiadis, J. G. (2020) · Physical review. E · doi:10.1103/physreve.102.043305 · Cited by 15
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Self-citationCharacterization and correction of time‐varying eddy currents for diffusion MRI
Valsamis, J. J., Dubovan, P. I., & Baron, C. A. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29124 · Cited by 14
Complex harmonics reveal low-dimensional manifolds of critical brain dynamics
Deco, G., Perl, Y. S., & Kringelbach, M. L. (2025) · Physical review. E · doi:10.1103/physreve.111.014410 · Cited by 14
Real valued diffusion‐weighted imaging using decorrelated phase filtering
Sprenger, T., Sperl, J. I., Fernandez, B., Haase, A., & Menzel, M. I. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26138 · Cited by 14
The Lamellar Structure of the Brain Fiber Pathways
Galinsky, V. L., & Frank, L. R. (2016) · Neural Computation · doi:10.1162/neco_a_00896 · Cited by 14
A 3D k‐space Fourier encoding and reconstruction framework for simultaneous multi‐slab acquisition
Dai, E., Wu, Y., Wu, W., Guo, R., Liu, S., Miller, K. L., Zhang, Z., & Guo, H. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27793 · Cited by 14
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Feasibility of diffusion weighting with a local inside‐out nonlinear gradient coil for prostate MRI
Bhuiyan, E., Dewdney, A., Weinreb, J. C., & Galiana, G. (2021) · Medical Physics · doi:10.1002/mp.15100 · Cited by 14
Metadata
- Authors
- Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Adad, J., McNab, J. A., Keil, B., Tisdall, M. D., Hoecht, P., Dietz, P., Cauley, S. F., Tountcheva, V., Matschl, V., Lenz, V. H., Heberlein, K., Potthast, A., Thein, H., Van Horn, J., Toga, A., Schmitt, F., Lehne, D., Rosen, B. R., Wedeen, V., & Wald, L. L.
- Affiliations
- Harvard University (US)
- Siemens Healthineers (Germany) (DE)
- University of California, Los Angeles (US)
- Corresponding author
- Wald, L.L.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 220-233
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3725309
- OpenAlex
- W1987263024
- Cited by
- 545
- References
- 69
- Citations by year
- 2026: 28 · 2025: 49 · 2024: 41 · 2023: 44 · 2022: 42 · 2021: 61 · 2020: 53 · 2019: 39 · 2018: 39 · 2017: 40 · 2016: 37 · 2015: 51 · 2014: 15 · 2013: 6
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- National Institutes of Health, NIH Blueprint Initiative for Neuroscience Research (U01MH093765, P41EB015896, K99/R00EB012107)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › NMR spectroscopy and applications
- Keywords
- diffusion MRI, Human Connectome Project, compressed sensing, diffusion spectrum imaging, high angular resolution diffusion imaging, signal-to-noise ratio, crossing fibers, diffusion tractography
- MeSH terms
- Animals, Brain, Humans, Image Enhancement, Models, Anatomic, Models, Neurological, Nerve Net, Diffusion Tensor Imaging, Connectome
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