Non-invasive Assessment of Pancreatic Duct Hypertension Using Computational Flow Modeling

To develop and validate a non-invasive method to estimate pancreatic duct pressure (PDP) by integrating magnetic resonance cholangiopancreatography (MRCP)-derived anatomy with computational flow modeling and to assess its clinical relevance for identifying pancreatic ductal hypertension (PDH) in chronic pancreatitis.

Three-dimensional pancreatic duct geometries were reconstructed from MRCP and used for computational fluid dynamics (CFD) simulations with physiologic wall-secretion boundary conditions. A quasi-one-dimensional analytical model was also derived to enable rapid PDP prediction from ductal geometry and flow. Prospective cases with endoscopic retrograde cholangiopancreatography (ERCP) manometry were used for validation, and a retrospective chronic pancreatitis (CP) cohort with documented pain-relief outcomes after decompression evaluated clinical utility.

Simulated intraductal pressure distributions localized to anatomical strictures and showed a positive linear correlation with ERCP-measured pressure drops in the prospective subset. In the retrospective cohort, greater simulated pressure drops were associated with larger postoperative pain-relief scores. The quasi-1D model closely matched CFD across flow regimes and improved further with a non-linear inertial term.

MRCP-based computational modeling enables non-invasive PDP estimation with promising agreement against invasive measurements and clinically meaningful association with symptom relief. The validated quasi-1D model supports rapid, clinically scalable assessment of PDH to inform patient selection and treatment planning.