King's College London, School of Biomedical Engineering and Imaging Sciences, London, United Kingdom.
Philips Healthcare, Guilford, United Kingdom.
Magn Reson Med. 2019 Feb;81(2):947-961. doi: 10.1002/mrm.27448. Epub 2018 Sep 3.
Develop a method for rigid body motion-corrected magnetic resonance fingerprinting (MRF).
MRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC-MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high-quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase shifts; and (4) motion-corrected data are reconstructed with low-rank inversion. MC-MRF was validated in a standard T /T phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through-plane motion in 2D MC-MRF was investigated.
Simulation results show that motion in MRF can introduce artifacts in T and T maps, depending when it occurs. MC-MRF improved parametric map quality in all phantom and in vivo experiments with in-plane motion, comparable to the no-motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through-plane motion, particularly for smaller structures in T maps.
Here, a novel method for motion correction in MRF (MC-MRF) is proposed, which improves parametric map quality and accuracy in comparison to no-motion correction approaches. Future work will include validation of 3D MC-MRF to enable also through-plane motion correction.
开发一种用于刚体运动校正磁共振指纹成像(MRF)的方法。
MRF 已经显示出在采集结束时对突然运动具有一定的鲁棒性。在这里,我们研究了在采集过程中不同类型的刚体运动对 MRF 的影响,并提出了一种校正这种运动的新方法。所提出的方法(MC-MRF)遵循以下 4 个步骤:(1)进行滑动窗口重建以产生高质量的辅助动态图像;(2)通过图像配准从动态图像中估计旋转和平移运动;(3)使用估计的运动对采集的 k 空间数据进行相应的旋转和相移校正;(4)使用低秩反演重建运动校正数据。在一个标准的 T/T 体模和 7 个健康受试者的 2D 活体脑采集实验中验证了 MC-MRF。此外,还研究了 2D MC-MRF 中面内运动的影响。
模拟结果表明,MRF 中的运动可以根据其发生的时间在 T 和 T 图中引入伪影。MC-MRF 改善了所有体模和体内实验中存在面内运动的参数图质量,与无运动的真实情况相当。对于存在面内运动的采集,即使在运动校正后,参数图质量也会降低,特别是在 T 图中较小的结构。
提出了一种用于 MRF 中运动校正的新方法(MC-MRF),与无运动校正方法相比,该方法提高了参数图的质量和准确性。未来的工作将包括验证 3D MC-MRF,以实现也对面内运动的校正。