Advances in computational and statistical diffusion MRI. (14th November 2017)

• Record Type: Journal Article
• Authors: O'Donnell, Lauren J.; Daducci, Alessandro; Wassermann, Demian; Lenglet, Christophe; Leemans, Alexander
• Is Part Of: NMR in biomedicine. Volume 32:Number 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-11-14
• Subjects: diffusion MRI -- registration -- statistics -- tractography; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.3805
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole‐brain connectivity information that describes the brain's wiring diagram and population‐based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high‐level overview of interest to diffusion MRI researchers, with a more in‐depth treatment to illustrate selected computational advances. Abstract : Computational analyses of diffusion MRI can provide rich data about the brain, including tissue microstructure measures, whole‐brain connectivity information and population‐based statistical analyses. In this review, we highlight recent computational and statistical advances in diffusion MRI and put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use.