Evidence map›Paper›PMID 41718850›Full record

ArticleAnnals of biomedical engineering2026

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

Haobo Zhao, Jung-Hee Seo, Venkata Akshintala, Surya Evani, Rajat Mittal

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Haobo ZhaoMechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA.
Jung-Hee SeoMechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA.
Venkata AkshintalaDepartment of Gastroenterology, Johns Hopkins Hospital, 600 N Wolfe St., Baltimore, MD, 21287, USA.
Surya EvaniDepartment of Gastroenterology, Johns Hopkins Hospital, 600 N Wolfe St., Baltimore, MD, 21287, USA.
Rajat MittalMechanical Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, 21218, USA. mittal@jhu.edu.ORCID http://orcid.org/0000-0002-8107-9499

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo 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.

methodsThree-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.

resultsSimulated 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.

conclusionMRCP-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.

Indexed as

Models, BiologicalPancreatic DuctsPancreatitis, ChronicCholangiopancreatography, Endoscopic RetrogradeCholangiopancreatography, Magnetic ResonanceComputer SimulationFemaleHumansHydrodynamicsMaleMiddle AgedChronic pancreatitisComputational biomechanicsComputational fluid dynamicsMRCPPancreatic ductPhysiological flow

Identifiers

PMID41718850

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.