Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, and Department of Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
Neuroimage. 2012 Jan 2;59(1):389-98. doi: 10.1016/j.neuroimage.2011.07.004. Epub 2011 Jul 13.
Diffusion-weighting in magnetic resonance imaging (MRI) increases the sensitivity to molecular Brownian motion, providing insight in the micro-environment of the underlying tissue types and structures. At the same time, the diffusion weighting renders the scans sensitive to other motion, including bulk patient motion. Typically, several image volumes are needed to extract diffusion information, inducing also inter-volume motion susceptibility. Bulk motion is more likely during long acquisitions, as they appear in diffusion tensor, diffusion spectrum and q-ball imaging. Image registration methods are successfully used to correct for bulk motion in other MRI time series, but their performance in diffusion-weighted MRI is limited since diffusion weighting introduces strong signal and contrast changes between serial image volumes. In this work, we combine the capability of free induction decay (FID) navigators, providing information on object motion, with image registration methodology to prospectively--or optionally retrospectively--correct for motion in diffusion imaging of the human brain. Eight healthy subjects were instructed to perform small-scale voluntary head motion during clinical diffusion tensor imaging acquisitions. The implemented motion detection based on FID navigator signals is processed in real-time and provided an excellent detection performance of voluntary motion patterns even at a sub-millimetre scale (sensitivity≥92%, specificity>98%). Motion detection triggered an additional image volume acquisition with b=0 s/mm2 which was subsequently co-registered to a reference volume. In the prospective correction scenario, the calculated motion-parameters were applied to perform a real-time update of the gradient coordinate system to correct for the head movement. Quantitative analysis revealed that the motion correction implementation is capable to correct head motion in diffusion-weighted MRI to a level comparable to scans without voluntary head motion. The results indicate the potential of this method to improve image quality in diffusion-weighted MRI, a concept that can also be applied when highest diffusion weightings are performed.
磁共振成像（MRI）中的扩散加权可以提高对分子布朗运动的敏感性，从而深入了解基础组织类型和结构的微环境。同时，扩散加权会使扫描对其他运动敏感，包括患者的整体运动。通常，需要多个图像体积来提取扩散信息，这也会导致体积间运动敏感性。在较长的采集过程中更容易出现整体运动，因为它们出现在扩散张量、扩散谱和 q 球成像中。图像配准方法成功地用于校正其他 MRI 时间序列中的整体运动，但在扩散加权 MRI 中的性能受到限制，因为扩散加权会在连续图像体积之间引入强烈的信号和对比度变化。在这项工作中，我们结合了自由感应衰减（FID）导航器的功能，该导航器提供了关于物体运动的信息，以及图像配准方法，以便在人类大脑的扩散成像中前瞻性地或可选地回顾性地校正运动。八名健康受试者被指示在临床扩散张量成像采集期间进行小范围的自愿头部运动。基于 FID 导航器信号的运动检测是实时进行的，即使在亚毫米级（灵敏度≥92%，特异性>98%）的尺度上，也能提供出色的自愿运动模式检测性能。运动检测触发了额外的 b=0 s/mm2 图像体积采集，随后将其与参考体积进行配准。在前瞻性校正方案中，计算出的运动参数被应用于实时更新梯度坐标系，以校正头部运动。定量分析表明，该运动校正实现能够将扩散加权 MRI 中的头部运动校正到与无自愿头部运动扫描相当的水平。结果表明，该方法有可能改善扩散加权 MRI 的图像质量，这一概念也可应用于最高扩散权重的情况。
