{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:52:50Z","timestamp":1791478370946,"version":"4.3.4"},"reference-count":57,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["5R01CA237597"],"award-info":[{"award-number":["5R01CA237597"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["5R01CA196701"],"award-info":[{"award-number":["5R01CA196701"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microcirculation"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Reconstruction of vascular topology from imperfect 3D imaging data is essential for accurate hemodynamic simulations. Therefore, we developed a \u201cgap\u2010filling\u201d method predicated on vascular biology and empirical patterns to restore incomplete vessels, independent of the imaging modality and spatial resolution employed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our gap\u2010filling algorithm reconstructs a vascular network based on distributions of its extant morphological features and employs different rules for healthy and tumor tissue. Algorithm performance was evaluated on (i) incomplete synthetic vascular trees with simulated CT noise, (ii) micro\u2010CT\u2010acquired brain tumor vasculature, (iii) micro\u2010MRI\u2010derived vasculature from a healthy mouse brain, and (iv) vasculature from the murine hippocampus imaged with light\u2010sheet microscopy (LSM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For synthetic vascular trees, our algorithm restored more gaps when the volume fraction was between 80% and 95%. Peak vessel segment recovery occurred for intermediate volume fractions (86%\u201389%). Gap\u2010filling performance was impacted when image noise was introduced. Across CT, MRI, and LSM, our method successfully reconnected missing vessel segments while preserving the overall features of the vascular network. Gap\u2010filling of tumor vasculature, with its abnormal topology, was better when tumor\u2010specific reconstruction rules were employed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our method showed efficient restoration of vascular network connectivity for 3D data derived from common imaging modalities at different resolutions. We also demonstrated the significance of applying distinct biologically\u2010informed rules when reconstructing healthy and tumor vascular networks.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/micc.70086","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:04:33Z","timestamp":1791475473000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Empirically Informed Blood Vessel Network Reconstruction for Multimodality Image\u2010Based Vascular Systems Biology"],"prefix":"10.1111","volume":"33","author":[{"given":"Yiyang","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering The Johns Hopkins University Whiting School of Engineering  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6813-3091","authenticated-orcid":false,"given":"Reshmi","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Texas  Austin Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6706-9235","authenticated-orcid":false,"given":"Aleksander S.","family":"Popel","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering The Johns Hopkins University Whiting School of Engineering  Baltimore Maryland USA"},{"name":"Sidney Kimmel Comprehensive Cancer Center The Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8297-6055","authenticated-orcid":false,"given":"Akanksha","family":"Bhargava","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering University of New South Wales  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2068-9445","authenticated-orcid":false,"given":"Arvind P.","family":"Pathak","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering The Johns Hopkins University Whiting School of Engineering  Baltimore Maryland USA"},{"name":"Sidney Kimmel Comprehensive Cancer Center The Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Russell H. 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