{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:40:17Z","timestamp":1792453217686,"version":"4.5.0"},"reference-count":0,"publisher":"IOP Publishing","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Michigan Technological University Health Research Institute"},{"DOI":"10.13039\/100000968","name":"American Heart Association","doi-asserted-by":"publisher","award":["25AIREA1377168"],"award-info":[{"award-number":["25AIREA1377168"]}],"id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institutes of Health, USA","award":["1R15HL172198"],"award-info":[{"award-number":["1R15HL172198"]}]},{"name":"National Institutes of Health, USA","award":["1R15HL173852"],"award-info":[{"award-number":["1R15HL173852"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Purpose of Review Imaging-derived fractional flow reserve (FFR) is rapidly evolving beyond conventional computational fluid dynamics (CFD)-based pipelines toward machine learning (ML), deep learning (DL), and physics-informed approaches that enable fast, wire-free, and scalable functional assessment of coronary artery stenosis. This review synthesizes recent advances in computed tomography (CT)-and angiography-based FFR measurement, with particular emphasis on emerging physics-informed neural networks and neural operators (PINNs and PINOs), as well as key considerations for their clinical translation.Recent Findings ML\/DL approaches have markedly improved automation and computational speed, enabling prediction of pressure and FFR from anatomical descriptors or angiographic contrast dynamics. However, their real-world performance and generalizability can remain variable and sensitive to domain shift, due to multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality. Physics-informed learning introduces conservation structure and boundary-condition consistency into model training, improving generalizability and reducing dependence on dense supervision while maintaining rapid inference. Recent evaluation trends increasingly highlight deployment-oriented metrics, including calibration, uncertainty quantification, and quality-control gatekeeping, as essential for safe clinical use.SummaryThe field is converging toward imaging-derived FFR methods that are faster, more automated, and more reliable. While ML\/DL offers substantial efficiency gains, physics-informed frameworks such as PINNs and PINOs may provide a more robust balance between speed and physical consistency. Prospective multi-center validation and standardized evaluation will be critical to support broad and safe clinical adoption.<\/jats:p>","DOI":"10.1088\/1361-6560\/aeb642","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T22:52:23Z","timestamp":1792450343000},"source":"Crossref","is-referenced-by-count":0,"title":["Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods"],"prefix":"10.1088","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7701-0645","authenticated-orcid":false,"given":"Tanxin","family":"Zhu","sequence":"first","affiliation":[{"name":"Michigan Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5062-9796","authenticated-orcid":false,"given":"Emran","family":"Hossen","sequence":"additional","affiliation":[{"name":"Michigan Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5782-3329","authenticated-orcid":false,"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Kennesaw State University Marietta Campus"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7205-0328","authenticated-orcid":false,"given":"Jingfeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Michigan Technological University College of Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Esposito","sequence":"additional","affiliation":[{"name":"Medical University of South Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4105-8463","authenticated-orcid":false,"given":"Jiguang","family":"Sun","sequence":"additional","affiliation":[{"name":"Michigan Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-959X","authenticated-orcid":false,"given":"Weihua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Michigan Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,19]]},"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb642","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb642\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb642\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb642\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T22:52:24Z","timestamp":1792450344000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,19]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/aeb642","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,19]]}}}