{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T16:35:33Z","timestamp":1789835733267,"version":"4.0.1"},"reference-count":31,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1663747"],"award-info":[{"award-number":["1663747"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2310910"],"award-info":[{"award-number":["2310910"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["JRSM Cardiovascular Disease"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:p>Patient-specific cardiovascular simulations are an integral part of cardiovascular research and are increasingly finding use in clinical practice. A primary factor hindering large cohort studies and clinical utilization of cardiovascular simulations is the time-consuming process of constructing simulation-suitable computer models from medical image data. Methods have been proposed to automate the model construction process, but are tailored to either vascular or cardiac models. We herein propose a framework, MeshGrow, that is capable of combining automated modeling of cardiac and extended vascular territories. Namely, we demonstrate that this method can be used to reconstruct the main cardiac chambers along with the aorta and its main sub-branches, and returns a simulation-suitable mesh with a defined aortic valve surface and other surfaces needed for boundary condition specification. MeshGrow combines two different machine learning based techniques to address the specific challenges involved with cardiac and vascular model construction. This results in a two-stage approach; (a) meshing the cardiac structures and (b) growing the vasculature out from it. We present results of our method on five CT test datasets and compare predictions against state-of-the-art benchmark methods, as well as ground truth manually constructed models. Results show that MeshGrow achieves higher metric scores than benchmark methods on the test data. Additionally, we showcase the applicability of the method by running three-dimensional computational fluid dynamics simulations with two of the test cases. With this work, we demonstrate a method that can automate the construction of combined cardiac and vascular models for patient-specific cardiovascular hemodynamic simulation.<\/jats:p>","DOI":"10.1177\/20480040261455944","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T12:26:12Z","timestamp":1783772772000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["MeshGrow: Integrated framework for simulation-ready cardiac and vascular mesh construction from medical imaging"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6389-6631","authenticated-orcid":false,"given":"Numi","family":"Sveinsson Cepero","sequence":"first","affiliation":[{"name":"University of California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arjun","family":"Narayanan","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-565X","authenticated-orcid":false,"given":"Fanwei","family":"Kong","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7561-1568","authenticated-orcid":false,"given":"Shawn C.","family":"Shadden","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12265-018-9792-2"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1747-0803.2010.00383.x"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-27354-w"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008881"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-016-1762-8"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13239-020-00497-5"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-019-02029-3"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2011.06.009"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-015-0033-5"},{"key":"e_1_3_3_12_2","first-page":"111011","article-title":"Automating model generation for image-based cardiac flow simulation","volume":"142","author":"Kong F","year":"2020","unstructured":"Kong F, Shadden SC. Automating model generation for image-based cardiac flow simulation. 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