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Cited by 545Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

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Setsompop, K., Kimmlingen, R., Eberlein, E., Witzel, T., Cohen-Adad, J., McNab, J.A., Keil, B., Tisdall, M.D. +16 more·2013·NeuroImage
DOI 10.1016/j.neuroimage.2013.05.078PMC PMC3725309
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Abstract

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.

    “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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Research works printed but not linked: 7

  1. Andersson J Xu J Yacoub E Auerbach E Moeller S Ugurbil K 2012 A comprehensive gaussian process framework for correcting distortions and movements in diffusion images Proc. of the ISMRM Melbourne Australia
  2. Cox E Gowland P 2008 Measuring T2 and T2′ in the brain at 1.5T, 3T and 7T using a hybrid gradient echo-spin echo sequence and EPI Proceedings of the ISMRM Toronto
  3. Hebrank FX Gebhardt M 2000 SAFE Model – a new method for predicting peripheral nerve stimulations in MRI Proceedings of the ISMRM Denver USA
  4. Lee N Singh M 2010 Compressed sensing based diffusion spectrum imaging Proc. of the ISMRM Stockholm Sweden
  5. Merlet S Caruyer E Deriche R 2012 Parametric dictionary learning for modeling EAP and ODF in diffusion MRI Med Image Comput Comput Assist Interv 15 pt2 288 296 23286108
  6. Moeller S 2012 Proc. of the ISMRM Melbourne Austrailia
  7. Nunes RG Hajnal JV Golay X Larkman DJ 2006 Simultaneous slice excitation and reconstruction for single-shot EPI Proc. of ISMRM

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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)
DOI
10.1016/j.neuroimage.2013.05.078
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
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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 › Physics and Astronomy › Nuclear and High Energy Physics › 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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