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

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.