Corrado Philip A, Seiter Daniel P, Wieben Oliver
Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
Departments of Medical Physics and Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Int J Comput Assist Radiol Surg. 2022 Jan;17(1):199-210. doi: 10.1007/s11548-021-02475-1. Epub 2021 Aug 17.
Despite the great potential and flexibility of 4D flow MRI for hemodynamic analysis, a major limitation is the need for time-consuming and user-dependent post-processing. We propose a fast four-step algorithm for rapid, robust, and repeatable flow measurements in the great vessels based on automatic placement of measurement planes and vessel segmentation.
Our algorithm works by (1) subsampling the 3D image into 3D patches, (2) predicting the probability of each patch containing individual vessels and location/orientation of the vessel within the patch via a convolutional neural network, (3) selecting the predicted planes with highest probabilities for each vessel, and (4) shifting the plane centers to the maximum velocity within each plane. The method was trained on 283 scans and evaluated on 40 unseen scans by comparing algorithm-derived processing times, plane locations, and flow measurements to those of two manual observers (graduate students) using t-tests, Pearson correlation, and Bland-Altman analysis.
The average processing time for the algorithm (18 s) was shorter than observer 1 (362 s; P < 0.001) and observer 2 (317 s; P < 0.001). The distance between planes placed by the algorithm and those placed by manual observers was slightly greater (O1 vs. algorithm: 9.0 mm, O2 vs. algorithm: 10.3 mm) than the distance between planes placed by the two manual observers (8.3 mm). The correlation between flow values for planes placed by the algorithm and those placed by manual observers was slightly lower (O1 vs. algorithm: R = 0.68, O2 vs. algorithm: R = 0.72) than the flow correlation between the two manual observers (R = 0.81).
Our method is a feasible and accurate approach for fast, reproducible, and automated flow measurement and visualization in 4D flow MRI of the great vessels, with similar variability compared to a manual annotator as the variability between two manual observers. This approach could be applied in other anatomical regions.
尽管4D流MRI在血流动力学分析方面具有巨大潜力和灵活性，但一个主要限制是需要耗时且依赖用户的后处理。我们提出了一种快速的四步算法，基于测量平面的自动放置和血管分割，在大血管中进行快速、稳健且可重复的血流测量。
我们的算法通过以下步骤工作：(1) 将3D图像下采样为3D补丁；(2) 通过卷积神经网络预测每个补丁包含单个血管的概率以及血管在补丁内的位置/方向；(3) 为每个血管选择概率最高的预测平面；(4) 将平面中心移动到每个平面内的最大速度处。该方法在283次扫描上进行训练，并通过t检验、Pearson相关性分析和Bland-Altman分析，将算法得出的处理时间、平面位置和血流测量结果与两名人工观察者（研究生）的结果进行比较，在40次未见过的扫描上进行评估。
算法的平均处理时间（18秒）短于观察者1（362秒；P < 0.001）和观察者2（317秒；P < 0.001）。算法放置的平面与人工观察者放置的平面之间的距离（观察者1与算法：9.0毫米，观察者2与算法：10.3毫米）略大于两名人工观察者放置的平面之间的距离（8.3毫米）。算法放置的平面的血流值与人工观察者放置的平面的血流值之间的相关性（观察者1与算法：R = 0.68，观察者2与算法：R =
