Diffusion Imaging in the Post-HCPEra

Steen Moeller, Pramod Pisharady Kumar, Jesper Andersson, Mehmet Akcakaya, Noam Harel, Ruoyun Ma, Xiaoping Wu, Essa Yacoub, Christophe Lenglet, Kamil Ugurbil

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
JournalJournal of Magnetic Resonance Imaging
DOIs
StateAccepted/In press - 2020

Keywords

  • diffusion imaging
  • human connectome project
  • ultra high field

PubMed: MeSH publication types

  • Journal Article
  • Review

Fingerprint Dive into the research topics of 'Diffusion Imaging in the Post-HCPEra'. Together they form a unique fingerprint.

Cite this