Computer Science Department, Faculty of Sciences, University of Geneva, Switzerland.
Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland; CIBM Center for Biomedical Imaging, MRI HUG-UNIGE, Geneva, Switzerland.
Phys Med. 2022 Oct;102:79-87. doi: 10.1016/j.ejmp.2022.09.005. Epub 2022 Sep 19.
MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either prospective or retrospective. Benchmarking the latter methods requires labeled motion-corrupted datasets, which are uncommon. Up to our best knowledge, no protocol for generating labeled datasets of MRI images corrupted by controlled motion has yet been proposed. Hence, we present a methodology allowing the acquisition of reproducible motion-corrupted MRI images as well as validation of the system's performance by motion estimation through rigid-body volume registration of fast 3D echo-planar imaging (EPI) time series. A proof-of-concept is presented, to show how the protocol can be implemented to provide qualitative and quantitative results. An MRI-compatible video system displays a moving target that volunteers equipped with customized plastic glasses must follow to perform predefined head choreographies. Motion estimation using rigid-body EPI time series registration demonstrated that head position can be accurately determined (with an average standard deviation of about 0.39 degrees). A spatio-temporal upsampling and interpolation method to cope with fast motion is also proposed in order to improve motion estimation. The proposed protocol is versatile and straightforward. It is compatible with all MRI systems and may provide insights on the origins of specific motion artifacts. The MRI and artificial intelligence research communities could benefit from this work to build in-vivo labeled datasets of motion-corrupted MRI images suitable for training/testing any retrospective motion correction or machine learning algorithm.
MRI 是一种对患者运动敏感的非侵入性医学成像方式，这在大多数临床应用中构成了主要限制。解决方案可能来自于减少采集时间或运动校正技术，无论是前瞻性还是回顾性的。基准测试后者方法需要标记的运动污染数据集，这是不常见的。据我们所知，目前还没有提出用于生成受受控运动污染的 MRI 图像的标记数据集的协议。因此，我们提出了一种允许获取可重复的运动污染 MRI 图像的方法，以及通过刚体体积配准快速 3D 回波平面成像 (EPI) 时间序列来验证系统性能的运动估计。提出了一个概念验证，以展示如何实施该协议以提供定性和定量结果。一个与 MRI 兼容的视频系统显示一个移动目标，志愿者必须配备定制的塑料眼镜来遵循，以执行预定义的头部动作。使用刚体 EPI 时间序列配准进行的运动估计表明，可以准确确定头部位置（平均标准偏差约为 0.39 度）。还提出了一种时空上采样和插值方法来应对快速运动，以提高运动估计的准确性。所提出的协议是多功能且简单的。它与所有 MRI 系统兼容，并可以深入了解特定运动伪影的起源。MRI 和人工智能研究社区可以从这项工作中受益，构建适合训练/测试任何回顾性运动校正或机器学习算法的受体内标记数据集。
