{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:15:59Z","timestamp":1785838559493,"version":"3.56.0"},"reference-count":34,"publisher":"IOP Publishing","issue":"24","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"crossref","award":["R21EB028954"],"award-info":[{"award-number":["R21EB028954"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,12,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Head motion correction (MC) is an essential process in brain positron emission tomography (PET) imaging. We have used the Polaris Vicra, an optical hardware-based motion tracking (HMT) device, for PET head MC. However, this requires attachment of a marker to the subject\u2019s head. Markerless HMT (MLMT) methods are more convenient for clinical translation than HMT with external markers. In this study, we validated the United Imaging Healthcare motion tracking (UMT) MLMT system using phantom and human point source studies, and tested its effectiveness on eight\n                    <jats:sup>18<\/jats:sup>\n                    F-FPEB and four\n                    <jats:sup>11<\/jats:sup>\n                    C-LSN3172176 human studies, with frame-based region of interest (ROI) analysis. We also proposed an evaluation metric, registration quality (\n                    <jats:italic>RQ<\/jats:italic>\n                    ), and compared it to a data-driven evaluation method, motion-corrected centroid-of-distribution (MCCOD).\n                    <jats:italic>Approach.<\/jats:italic>\n                    UMT utilized a stereovision camera with infrared structured light to capture the subject\u2019s real-time 3D facial surface. Each point cloud, acquired at up to 30 Hz, was registered to the reference cloud using a rigid-body iterative closest point registration algorithm.\n                    <jats:italic>Main results.<\/jats:italic>\n                    In the phantom point source study, UMT exhibited superior reconstruction results than the Vicra with higher spatial resolution (0.35 \u00b1 0.27 mm) and smaller residual displacements (0.12 \u00b1 0.10 mm). In the human point source study, UMT achieved comparable performance as Vicra on spatial resolution with lower noise. Moreover, UMT achieved comparable ROI values as Vicra for all the human studies, with negligible mean standard uptake value differences, while no MC results showed significant negative bias. The\n                    <jats:italic>RQ<\/jats:italic>\n                    evaluation metric demonstrated the effectiveness of UMT and yielded comparable results to MCCOD.\n                    <jats:italic>Significance.<\/jats:italic>\n                    We performed an initial validation of a commercial MLMT system against the Vicra. Generally, UMT achieved comparable motion-tracking results in all studies and the effectiveness of UMT-based MC was demonstrated.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad0e37","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T17:29:46Z","timestamp":1700501386000},"page":"245019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Markerless head motion tracking and event-by-event correction in brain PET"],"prefix":"10.1088","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5467-062X","authenticated-orcid":false,"given":"Tianyi","family":"Zeng","sequence":"first","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Yale University"},{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weize","family":"Jiang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxu","family":"Zheng","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Gravel","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Wan","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn","family":"Fontaine","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Mulnix","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Jiang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Yang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enette Mae","family":"Revilla","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mika","family":"Naganawa","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuya","family":"Toyonaga","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shannan","family":"Henry","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuoyu","family":"Cao","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhi","family":"Hu","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare, Houston, TX,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard E","family":"Carson","sequence":"additional","affiliation":[{"name":"Yale University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"pmbad0e37bib1","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","type":"journal-article","article-title":"A method for registration of 3D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. 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