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Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques.

Abstract：

:In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Graham MS,Drobnjak I,Zhang H

doi

10.1016/j.neuroimage.2015.11.006

subject

Has Abstract

pub_date

2016-01-15 00:00:00

pages

1079-1094

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(15)01028-9

journal_volume

125

pub_type

杂志文章

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Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques.