{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:05:48Z","timestamp":1790697948776,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T00:00:00Z","timestamp":1780185600000},"content-version":"vor","delay-in-days":30,"URL":"http:\/\/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":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5P30 CA023108"],"award-info":[{"award-number":["5P30 CA023108"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE\u20102125733"],"award-info":[{"award-number":["DGE\u20102125733"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cherenkov imaging provides the ability to visualize beam delivery during radiation therapy treatments in real\u2010time informing the therapy team about possible deviations from the treatment plan. Current clinical practice with Cherenkov imaging relies on the real\u2010time view and post\u2010treatment qualitative assessment of Cherenkov images by therapists, physicists and clinicians who may then perform secondary treatment checks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While Cherenkov images provide new insights into beam location, the limitation is that quantifying the observed deviation is difficult due to the lack of spatial registration of the Cherenkov emission in patient's coordinate space. This work addresses this problem with the first fusion of Cherenkov images with the three\u2010dimensional patient surface to enable quantitative spatial beam verification in patient coordinate space. By combining multiple camera viewpoints with CT\u2010sim derived surface coordinates, this system is capable of generating virtual beam's eye view images for objective measurement of beam delivery deviations on patient surfaces. This approach enables automated error detection thresholds and represents the first step toward real\u2010time, quantitative optical treatment verification and dosimetry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Cherenkov images from multiple geometrically calibrated cameras were projected onto Computed Tomography (CT) surface datasets to provide a Cherenkov surface image map on the patient. A raycasting algorithm was utilized to virtually map the Cherenkov surface image into the beam's eye view. To test the ability to quantitatively measure beam versus patient surface deviations from the virtual beam's eye view, a series of 10\u00a0\u00d7\u00a010 cm\n                      <jats:sup>2<\/jats:sup>\n                      6X fields with simulated spatial shifts were delivered to both flat and anthropomorphic phantoms. Virtual beam's eye view images were generated for each shift, and the magnitudes of Cherenkov map shifts were measured with Iterative Closest Point (ICP) and Hausdorff distance metrics with respect to a reference field.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Iterative Closest Point algorithms achieved an average x and y accuracy of 0.5\u00a0\u00b1\u00a00.2\u00a0mm and 0.3\u00a0\u00b1\u00a00.2\u00a0mm for the flat phantom and 0.5\u00a0mm\u00a0\u00b1\u00a00.7\u00a0mm and 0.3\u00a0mm\u00a0\u00b1\u00a00.2\u00a0mm for the anthropomorphic torso phantom. Camera projection error adds up to an additional 0.9\u00a0mm of error.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multi\u2010view surface mapping of Cherenkov images onto the patient surface mesh enabled quantitative assessment of beam versus topology shifts between fractions, using the Cherenkov emission outline as a beam extent surrogate. We demonstrated the utility and metrics of Cherenkov 3D mapping towards future applications utilizing daily or real\u2010time surfaces obtained from, for example, cone\u2010beam CT or surface\u2010guided radiotherapy (SGRT) systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70566","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T04:22:15Z","timestamp":1780287735000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Three\u2010dimensional Cherenkov emission surface mapping into the patient coordinate system for spatial delivery verification in EBRT"],"prefix":"10.1002","volume":"27","author":[{"given":"Alexander","family":"Geiersbach","sequence":"first","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Jermyn","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Gladstone","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"},{"name":"Department of Radiation Oncology and Applied Sciences Geisel School of Medicine Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lesley","family":"Jarvis","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Applied Sciences Geisel School of Medicine Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Pogue","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petr","family":"Bruza","sequence":"additional","affiliation":[{"name":"Thayer School of Engineering Dartmouth College  Hanover New Hampshire USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,31]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.01.046"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2022.06.009"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2018.02.003"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4906249"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.11.013"},{"key":"e_1_2_10_7_1","unstructured":"ZhouQ\u2010Y ParkJ KoltunV Open3D: A Modern Library for 3D Data Processing. 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