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Abstract #2813

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

Myat Pwint Thein1,2, Chenglin Zhu2, Xinzi He3, Sreram Muthukannan1, Kaartikeya Gupta1, Yi Wang2,4, Keigo Kawaji1, and Martin R. Prince2
1Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, 2Radiology, Weill Cornell Medicine, New York, NY, United States, 3Radiology, Weill Cornell Medical College, New York, NY, United States, 4Biomedical Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: Urogenital, Bladder, Hydrometry, Time-Volume Curves, AI-segmentation

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.

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