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骨骼肌回波平面图像的运动与畸变校正

Motion and distortion correction of skeletal muscle echo planar images.

作者信息

Davis Andrew D, Noseworthy Michael D

机构信息

Department of Medical Physics and Applied Radiation Sciences, McMaster University, Hamilton, Ontario, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada.

Department of Medical Physics and Applied Radiation Sciences, McMaster University, Hamilton, Ontario, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada; School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada; Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada.

出版信息

Magn Reson Imaging. 2016 Jul;34(6):832-838. doi: 10.1016/j.mri.2016.03.003. Epub 2016 Mar 10.

Abstract

This paper examines two artifacts facing researchers who use gradient echo (GRE) echo planar imaging (EPI) for time series studies of skeletal muscles in limbs. The first is through-plane blood flow during the acquisition, causing a vessel motion artifact that inhibits proper motion correction of the data. The second is distortion of EPI images caused by B0 field inhomogeneities. Though software tools are available for correcting these artifacts in brain EPI images, the tools do not perform well on muscle images. The severity of the two artifacts was described using image similarity measures, and the data was processed with both a conventional motion correction program and custom written tools. The conventional program did not perform well on the limb images, in fact significantly degrading image quality in some trials. Data is presented which proves that arterial pulsatile signal caused the impairment in motion correction. The new tools were shown to perform much better, achieving substantial motion correction and distortion correction of the muscle EPI images.

摘要

本文探讨了使用梯度回波(GRE)回波平面成像(EPI)对四肢骨骼肌进行时间序列研究的研究人员所面临的两个伪影。第一个伪影是采集过程中的层面内血流,会导致血管运动伪影,从而抑制数据的正确运动校正。第二个伪影是由B0场不均匀性引起的EPI图像失真。尽管有软件工具可用于校正脑EPI图像中的这些伪影,但这些工具在肌肉图像上效果不佳。使用图像相似性度量描述了这两个伪影的严重程度,并使用传统的运动校正程序和自定义编写的工具对数据进行了处理。传统程序在肢体图像上表现不佳,实际上在某些试验中显著降低了图像质量。文中给出的数据证明,动脉搏动信号导致了运动校正的受损。新工具表现得要好得多,实现了肌肉EPI图像的实质性运动校正和失真校正。

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