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Diffusion Imaging in the Post HCP Era
Author(s) -
Moeller Steen,
Pisharady Kumar Pramod,
Andersson Jesper,
Akcakaya Mehmet,
Harel Noam,
Ma RuoyunEmily,
Wu Xiaoping,
Yacoub Essa,
Lenglet Christophe,
Ugurbil Kamil
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27247
Subject(s) - computer science , human connectome project , context (archaeology) , diffusion mri , diffusion , artificial intelligence , field (mathematics) , deep learning , encoding (memory) , magnetic resonance imaging , neuroscience , functional connectivity , geology , medicine , paleontology , physics , mathematics , radiology , pure mathematics , biology , thermodynamics
Abstract 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