{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T18:28:36Z","timestamp":1789237716683,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB032801, R01EB028146, R01HL156818"],"award-info":[{"award-number":["R01EB032801, R01EB028146, R01HL156818"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"IM1\"><mml:msup><mml:mi>K<\/mml:mi><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">trans<\/mml:mi><\/mml:mrow><\/mml:mrow><\/mml:msup><\/mml:math><\/jats:inline-formula>, fractional extravascular extracellular volume <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"IM2\"><mml:msub><mml:mi>v<\/mml:mi><mml:mi>e<\/mml:mi><\/mml:msub><\/mml:math><\/jats:inline-formula>, and rate constant <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"IM3\"><mml:msub><mml:mi>k<\/mml:mi><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">ep<\/mml:mi><\/mml:mrow><\/mml:mrow><\/mml:msub><\/mml:math><\/jats:inline-formula>) achieved intraclass correlation coefficient and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> between 0.84\u20130.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fradi.2023.1168901","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T08:58:48Z","timestamp":1693817928000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept 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