Abstract #2814

Towards Dynamic MR Hydrometry Time-Volume Filling Curve Analysis of the Urinary Bladder Filling: Initial Feasibility

Myat Pwint Thein^1,2, Chenglin Zhu², Xinzi He³, Kaartikeya Gupta⁴, Dave Tailor¹, Sreram Muthukannan¹, Yi Wang^2,5, Martin R. Prince², and Keigo Kawaji¹

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, Illinois Institute of Technology, CHICAGO, IL, United States, ⁵Biomedical Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: Urogenital, Bladder

Motivation: Static hydrometric volumetric measures can aid organ health and function assessments. Artificial Intelligence-driven Segmentation can aid time-volume filling curve analysis that were previously impractical via manual tracing.

Goal(s): A dynamic MR hydrometric approach was developed for full time-volume curve analysis of urinary bladder filling.

Approach: Six clinically available T2w-based sequences were first examined. One was chosen for repeat continuous acquisition, and subject urinal bladder volume filling curves were analyzed.

Results: Inter-sequence protocol yielded observal measurement discrepancies; hence, a single sequence repeated acquistion was employed to yield three characteristic bladder filling profiles, all explainable in terms of volume-pressure relationship between the kidney and bladder.

Impact: Dynamic MR Hydrometry enabled by AI-enabled automated segmentation is proposed. Urine accmulation, which is both non-trivial and known to be highly variable, was successfully characerized in this initial feasibility work towards dynamic assessments with time-volume filling curve analysis.

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