Thomas Schultz Gemma Nedjati-Gilani Archana Venkataraman - Computational Diffusion MRI and Brain Connectivity

Springer International Publishing Switzerland 2014

Yogesh Rathi 1

Abstract

Diffusion magnetic resonance imaging (dMRI) is an important tool that allows non-invasive investigation of the neural architecture of the brain. Advanced dMRI protocols typically require a large number of measurements for accurately tracing the fiber bundles and estimating the diffusion properties (such as, FA). However, the acquisition time of these sequences is prohibitively large for pediatric as well as patients with certain types of brain disorders (such as, dementia). Thus, fast echo-planar imaging (EPI) acquisition sequences were proposed by the authors in [6, 16], which acquired multiple slices simultaneously to reduce scan time. The scan time in such cases drops proportionately to the number of simultaneous slice acquisitions (which we denote by R ). While preliminary results in [6, 16] showed good reproducibility, yet the effect of simultaneous acquisitions on long range fiber connectivity and diffusion measures such as FA, is not known. In this work, we use multi-tensor based fiber connectivity to compare data acquired on two subjects with different acceleration factors ( R = 1, 2, 3). We investigate and report the reproducibility of fiber bundles and diffusion measures between these scans on two subjects with different spatial resolutions, which is quite useful while designing neuroimaging studies.