School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK.
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany.
Sci Rep. 2022 Mar 30;12(1):5395. doi: 10.1038/s41598-022-09327-1.
The impact of fetal motion on phase contrast magnetic resonance imaging (PC-MRI) with metric optimized gating (MOG) remains unknown, despite being a known limitation to prenatal MRI. This study aims to describe the effect of motion on fetal flow-measurements using PC-MRI with MOG and to generate a scoring-system that could be used to predict motion-corrupted datasets at the time of acquisition. Ten adult volunteers underwent PC-MRI with MOG using a motion-device to simulate reproducible in-plane motion encountered in fetuses. PC-MRI data were acquired on ten fetuses. All ungated images were rated on their quality from 0 (no motion) to 2 (severe motion). There was no significant difference in measured flows with in-plane motion during the first and last third of sequence acquisition. Movement in the middle section of acquisition produced a significant difference while all referring ungated images were rated with a score of 2. Intra-Class-Correlation (ICC) for flow-measurements in adult and fetal datasets was lower for datasets with scores of 2. For fetal applications, the use of a simple three-point scoring system reliably identifies motion-corrupted sequences from unprocessed data at the time of acquisition, with a high score corresponding to significant underestimation of flow values and increased interobserver variability.
尽管胎儿运动是产前磁共振成像（MRI）的已知局限性，但它对相位对比磁共振成像（PC-MRI）与度量优化门控（MOG）的影响尚不清楚。本研究旨在描述使用具有 MOG 的 PC-MRI 测量运动对胎儿流动的影响，并生成一种评分系统，以便在采集时预测运动污染的数据集。十名成年志愿者使用运动设备接受了具有 MOG 的 PC-MRI，以模拟胎儿中遇到的可重复的平面内运动。对十名胎儿进行了 PC-MRI 数据采集。所有无门控图像根据其质量从 0（无运动）到 2（严重运动）进行评分。在序列采集的前 1/3 和最后 1/3 期间，平面内运动对测量流量没有显著差异。在采集的中间部分运动产生了显著差异，而所有参考无门控图像的评分均为 2。成人和胎儿数据集的流量测量的组内相关系数（ICC）对于评分 2 的数据集较低。对于胎儿应用，使用简单的三分评分系统可以从采集时的未处理数据中可靠地识别出运动污染的序列，高分为流量值的显著低估和观察者间变异性增加。
