上海科技大学知识管理系统

Objective: To develop dynamic MRU protocol that focuses on the bladder to capture ureteral jets and to automatically estimate frequency and duration of ureteral jets from the dynamic images. Methods: Between February and July 2023, we collected 51 sets of dynamic MRU data from 5 healthy subjects. To capture the entire longitudinal trajectory of ureteral jets, we optimized orientation and thickness of the imaging slice for dynamic MRU, and developed a deep-learning method to automatically estimate frequency and duration of ureteral jets from the dynamic images. Results: Among the 15 sets of images with different slice positioning, the positioning with slice thickness of 25 mm and orientation of 30 degrees was optimal. Of the 36 sets of dynamic images acquired with the optimal protocol, 27 sets or 2529 images were used to train a U-Net model for automatically detecting the presence of ureteral jets. On the other 9 sets or 760 images, accuracy of the trained model was found to be 84.9 %. Based on the results of automatic detection, frequency of ureteral jet in each set of dynamic images was estimated as 8.0 f 1.4 min- deviating from reference by-3.3 % f 10.0 %; duration of each individual ureteral jet was estimated as 7.3 f 2.8 s, deviating from reference by 2.4 % f 32.2 %. The accumulative duration of ureteral jets estimated by the method correlated well (with coefficient of 0.936) with the bladder expansion recorded in the dynamic images. Conclusions: The proposed method was capable of quantitatively characterizing ureteral jets, potentially providing valuable information on functional status of ureteral peristalsis.