目的:前瞻性运动校正(PMC)的有效性通常通过比较在有和没有PMC(NoPMC)的情况下获取的图像中的伪影来评估。然而,由于NoPMC图像不可用,这种方法不适用于临床环境。我们旨在开发一种模拟方法,以证明基于脂肪导航仪的PMC在T2加权MRI中改善血管周围空间(PVS)可见性的能力。
方法:来自两项早期研究的MRI数据集用于运动伪影模拟和评估PMC,包括T2加权的NoPMC和PMC图像。要模拟运动伪影,使用非均匀傅立叶变换从无伪影图像中计算出运动扰动位置的k空间数据,并在傅立叶逆变换之前将其错放到笛卡尔网格上。模拟重现运动引起的模糊的能力,铃声,使用侧脑室/白质边界的锐度评估重影伪影,傅里叶频谱中的振铃伪影幅度,和背景噪音,分别。使用白质中的PVS体积分数来反映其可见性。
结果:在模拟中,清晰度,PVS体积分数,背景噪声与运动评分呈显著负相关。在清晰度上发现了显著的相关性,振铃伪影幅度,以及模拟和真实NoPMC图像之间的PVS体积分数(p≤0.006)。相比之下,模拟和真实PMC图像之间的相关性降低且不显著(p≥0.48),提示PMC减少运动效应。
结论:提出的模拟方法是研究运动和PMC对PVS能见度影响的有效工具。PMC可以减少由运动伪影引起的PVS体积分数的系统偏差。
OBJECTIVE: The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI.
METHODS: MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation\'s ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.
RESULTS: In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.
CONCLUSIONS: The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.