Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques
Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques
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DOI：
10.1016/j.neuroimage.2015.11.006
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发表时间：
2016-01-15
期刊：
影响因子：
5.7
通讯作者：
Zhang, Hui
中科院分区：
文献类型：
--
作者：
Graham, Mark S.;Drobnjak, Ivana;Zhang, Hui
在本文中，我们展示了一个模拟框架，使扩散加权磁共振（DW-MR）图像的后处理方法的直接和定量比较。DW-MR数据集被用于一系列技术中，这些技术能够估计大脑中的局部微观结构和全局连通性。这些技术需要跨数据集完全对齐图像，但这种情况很少。人为因素，如涡流（EC）失真和运动导致图像之间的不对准，这损害了从它们获得的微观结构的措施的质量。存在许多方法和软件包来纠正这些伪影，其中一些已经成为事实上的标准，但没有一个经过严格的验证。在文献中，使用定性视觉测量或定量替代指标来评估改进的对齐。在这里，我们介绍了一个模拟框架，允许直接，定量评估的技术，使现有的和未来的方法进行客观的比较。DW-MR数据集是使用基于MRI采集物理学的过程生成的，该过程允许再现图像及其伪影的显著特征。我们以三种方式应用这个框架。首先，我们评估了最常用的人工制品校正方法，FSL的eddy_correct，并将其与最近提出的替代方法eddy进行比较。我们定量地证明，使用eddy_correct导致校正数据中的显著误差，而eddy能够提供更好的校正。其次，我们调查所需的数据集，以实现良好的校正与涡流，通过查看所需的方向的最小数量和比较推荐的全球采集等效的半球协议。最后，我们通过检查微观结构模型与真实的和模拟数据的拟合来研究校正质量的影响。(C)2015作者爱思唯尔公司出版
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. (C) 2015 The Authors. Published by Elsevier Inc.