In-Vivo Estimates of Axonal Characteristics Using Optimized Diffusion MRI Protocols for Single Fibre Orientation.
In: Jiang, T and Navab, N and Pluim, JPW and Viegever, MA, (eds.)
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2010, PT I.
(pp. 623 - 630).
This work presents diffusion MR protocols that allow estimation of axonal parameters like diameter and density in the live human brain. Previous approaches demand very high field experimental systems or suffer from long acquisition times and are therefore impractical for use in clinical studies. We propose a method that significantly reduces scan time by making use of the a-priori known fibre orientation in structures with well defined single fibre (SF) organisation like the corpus callosum (CC) and produces protocols that can be performed in under 25 minutes on a standard clinical system. Results from a computer simulation experiment show that our SF protocols can generate parameter estimates with similar precision to previously proposed orientation invariant (OI) protocols. Furthermore, we acquire the 20 minute long SF protocol and the 1. hour long OI protocol in a scan/rescan study on two healthy subjects and compare the axonal parameter maps from both protocols.
|Title:||In-Vivo Estimates of Axonal Characteristics Using Optimized Diffusion MRI Protocols for Single Fibre Orientation|
|Event:||13th International Conference on Medical Image Computing and Computer-Assisted Intervention|
|Location:||China Natl Convent Ctr, Beijing, PEOPLES R CHINA|
|Dates:||2010-09-20 - 2010-09-24|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > IoN - Neuroinflammation
UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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