Improved motion correction for functional MRI using an omnibus regression model.
Improved motion correction for functional MRI using an omnibus regression model.
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DOI：
10.1109/isbi45749.2020.9098688
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发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Montillo A
中科院分区：
文献类型：
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作者：
Raval V;Nguyen KP;Mellema C;Montillo A
在功能性磁共振成像采集过程中，头部运动可以显著污染神经信号，并在信号相关性中引入虚假的、距离相关的变化。这可能会严重混淆对发育、衰老和疾病的研究。以前抑制头部运动伪影的方法涉及到有害协变量的顺序回归，但这已被证明会重新引入伪影。我们提出了一种使用综合回归模型的新的运动校正管道，该模型通过使用性能最好的算法来估计每个伪影同时回归多个伪影，从而避免了这个问题。我们使用大型异构数据集（n=151）定量评估其对序列回归管道的运动伪影抑制性能，该数据集包括高运动受试者和多种疾病表型。所提出的串联回归管道显著降低了头部运动与功能连接之间的关联，同时在消除距离相关的头部运动伪影方面显著优于传统的顺序回归管道。
Head motion during functional Magnetic Resonance Imaging acquisition can significantly contaminate the neural signal and introduce spurious, distance-dependent changes in signal correlations. This can heavily confound studies of development, aging, and disease. Previous approaches to suppress head motion artifacts have involved sequential regression of nuisance covariates, but this has been shown to reintroduce artifacts. We propose a new motion correction pipeline using an omnibus regression model that avoids this problem by simultaneously regressing out multiple artifacts using the best performing algorithms to estimate each artifact. We quantitatively evaluate its motion artifact suppression performance against sequential regression pipelines using a large heterogeneous dataset (n=151) which includes high-motion subjects and multiple disease phenotypes. The proposed concatenated regression pipeline significantly reduces the association between head motion and functional connectivity while significantly outperforming the traditional sequential regression pipelines in eliminating distance-dependent head motion artifacts.