MR Hydrometric Urine Accumulation Monitoring with Automated Segmentation: Initial Feasibility and Stability Assessment of Clinical Protocol

Motivation: Treatment efficacy in Autosomal Dominant Polycystic Kidney Disease (ADPKD) can be better assessed by measuring bladder urine generation rates, enabled by precise MRI sequence timing for optimal reproducibility. Goal(s): To identify optimal timeframe that promises the minimum variation and stable urine output growth in the bladder. Approach: We demonstrated optimized sequence and parameters for pelvis scanning, focusing on bladder. Using deep learning segmentation, we measure urine accumulation at 1-minute intervals to track the dynamic process of bladder filling over time. Results: A 25-30 minute time window was identified as optimal for achieving stable urine growth rate, with accumulation stability influenced by hydration status. Impact: This study assists radiologists in assessing treatment efficacy in ADPKD through precise urine generation tracking, supported by the reproducible optimized protocol establishing a 25-30 minute timeframe for stable measurements.