An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.
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DOI：
10.1016/j.neuroimage.2015.10.019
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发表时间：
2016-01-15
期刊：
影响因子：
5.7
通讯作者：
Sotiropoulos SN
中科院分区：
文献类型：
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作者：
Andersson JLR;Sotiropoulos SN
在本文中，我们描述了一种方法，用于回顾性估计和校正涡流（EC）引起的失真和对象的运动扩散成像。此外，可以提供磁化率感应场，并将其以准确反映两个场（磁化率感应场和EC感应场）在受试者运动时表现不同的方式纳入计算中。该方法基于将各个体积配准到每个体积应该看起来像什么的无模型预测，从而使其能够用于高b值数据，其中不同体积中的对比度相差很大。此外，我们还表明，常用的线性EC模型不足以满足本文中使用的数据（在3 T Siemens Verio、3 T Siemens Connectome Skyra或7 T Siemens Connectome上使用Stejskal-Tanner梯度获取的高空间和角分辨率数据）。T Siemens Magnetome扫描仪）并且高阶模型的性能明显更好。该方法已经在广泛的实际应用中，并用于四个主要项目（WU-UMinn HCP，MGH HCP，英国生物银行和白厅研究），以纠正扭曲和受试者运动。我们提出了一种新的方法来校正涡流引起的失真和对象的移动扩散数据。结果表明，即使对于高b值数据（B = 7000），也可以实现可靠的校正。与基于相关比的配准（eddy_correct）的比较表明，新方法大大优于上级。
In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement. We present a new method for correction of eddy current-induced distortions and subject movement in diffusion data. It is based on alignment to predictions based on a Gaussian process The results indicate that one can achieve reliable corrections even for high b-value data (b = 7000). A comparison to correlation ratio based registration (eddy_correct) indicates that the new method is vastly superior.