UNLABELLED:The objective of this study was to develop an automatic image registration technique capable of compensating for kidney motion in renal perfusion MRI, to assess the effect of renal artery stenosis on the kidney parenchyma. MATERIALS AND METHODS:Images from 20 patients scheduled for a renal perfusion study were acquired using a 1.5 T scanner. A free-breathing 3D-FSPGR sequence was used to acquire coronal views encompassing both kidneys following the infusion of Gd-BOPTA. A two-step registration algorithm was developed, including a preliminary registration minimising the quadratic difference and a fine registration maximising the mutual information (MI) between consecutive image frames. The starting point for the MI-based registration procedure was provided by an adaptive predictor that was able to predict kidney motion using a respiratory movement model. The algorithm was validated against manual registration performed by an expert user. RESULTS:The mean distance between the automatically and manually defined contours was 2.95 ± 0.81 mm, which was not significantly different from the interobserver variability of the manual registration procedure (2.86 ± 0.80 mm, P = 0.80). The perfusion indices evaluated on the manually and automatically extracted perfusion curves were not significantly different. CONCLUSIONS:The developed method is able to automatically compensate for kidney motion in perfusion studies, which prevents the need for time-consuming manual image registration.

译文

取消标记:这项研究的目的是开发一种能够在肾脏灌注MRI中补偿肾脏运动的自动图像配准技术,以评估肾动脉狭窄对肾脏实质的影响。
材料与方法:使用1.5T扫描仪从计划进行肾脏灌注研究的20位患者中获取图像。自由呼吸的3D-FSPGR序列用于在注入Gd-BOPTA之后获取涵盖两个肾脏的冠状视图。开发了一种两步配准算法,包括使二次方差最小的预配准和使连续图像帧之间的互信息(MI)最大化的精细配准。基于MI的注册过程的起点由自适应预测器提供,该预测器能够使用呼吸运动模型预测肾脏运动。针对专家用户执行的手动注册对算法进行了验证。
结果:自动和手动定义的轮廓之间的平均距离为2.95±0.81 mm,与手动注册程序的观察者间差异(2.86±0.80 mm,P = 0.80)没有显着差异。在手动和自动提取的灌注曲线上评估的灌注指数没有显着差异。
结论:开发的方法能够在灌注研究中自动补偿肾脏运动,从而避免了耗时的手动图像配准。

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