Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States.
Hum Brain Mapp. 2024 Feb 1;45(2):e26570. doi: 10.1002/hbm.26570.
Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.
头部运动校正特别具有挑战性在扩散加权磁共振成像（dMRI）扫描由于在不同梯度强度和方向的图像对比度的剧烈变化。头部运动校正通常使用在 FSL 的 Eddy 中实现的高斯过程模型来执行。最近，引入了基于 3dSHORE 的 SHORELine 方法，不需要基于壳的采集，但以前没有进行基准测试。在这里，我们在软件纤维幻影的真实模拟上对这两种方法进行了全面评估，该模拟提供了已知的头部运动真实情况。我们证明了这两种方法都表现得非常好，但性能可能会受到采样方案以及头部运动的程度和头部运动校正前应用的去噪策略的影响。此外，我们发现 Eddy 受益于先用 MP-PCA 对数据进行去噪。总之，我们提供了迄今为止最广泛的 dMRI 头部运动校正基准测试，以及广泛的模拟数据和可重复的工作流程。
