Abstract: Esophageal bolus transport and esophageal mechanics are quantified from medical imaging data, such as dynamic magnetic resonance imaging (“MRI”) data, computed tomography (“CT”) data, or the like. A machine learning model is used to process geometric or other spatiotemporal parameters of a bolus imaged with medical imaging in order to estimate quantitative parameters of the bolus and/or esophagus, such as cross-sectional area, fluid velocity, and fluid pressure. From these values, other parameters can be computed, such as esophageal stiffness and active relaxation.

Abstract: A fluid mechanics-based analysis tool is implemented to compute pressure field data, fluid velocity data, and/or muscular work data in a lumen or other tubular organ or structure from planimetry and pressure data (e.g., measured using a balloon dilation or other planimetry catheter). In this way, flow data can be estimated, which are otherwise insensible from current planimetry catheter technologies due to economic and/or manufacturing limitations.