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Lett."],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>\n                    Breast tumor margin assessment requires rapid identification of regions of interest (ROIs) and detailed visualization of their microstructural alterations. Here, we report position-tracked handheld optical coherence tomography (PH-OCT) guided by projected wide-field near-infrared autofluorescence (PW-NIRAF) imaging to meet these requirements. The PW-NIRAF system comprises a near-infrared (NIR) imaging camera and a projector with calibrated fields of view, enabling ROI identification and projection of the corresponding ROI map over a 10 \u00d7 10\u2009cm\n                    <jats:sup>2<\/jats:sup>\n                    field of view (FOV). The PH-OCT system consists of a handheld gradient-index-rod-lens-based OCT probe for volumetric visualization of ROI microstructure, with each OCT volume covering 4 \u00d7 4 \u00d7 1\u2009mm\n                    <jats:sup>3<\/jats:sup>\n                    at 9.66\u2009\u03bcm axial and 17.54\u2009\u03bcm lateral resolution. Blue and green tapes used as color markers on the OCT-probe housing, together with a color camera and a deep-learning-based YOLOv11-pose model, enable real-time probe-tip localization for OCT volume acquisition across PW-NIRAF-projected ROIs, providing depth-resolved microstructural information beyond wide-field NIRAF contrast. Color-camera\/projector calibration further enables back-projection of OCT-derived tumor-region maps onto the tissue surface. Validation on ex vivo breast tissue lays the foundation for future intraoperative breast tumor margin assessment using PW-NIRAF-guided PH-OCT.\n                  <\/jats:p>","DOI":"10.1364\/ol.600787","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T12:00:10Z","timestamp":1781179210000},"page":"3745","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Position-tracked handheld optical coherence tomography guided by projected wide-field near-infrared autofluorescence imaging for breast tumor margin assessment"],"prefix":"10.1364","volume":"51","author":[{"given":"Shuyu","family":"Chen","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4086-9566","authenticated-orcid":true,"given":"Ruoyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houpu","family":"Yang","sequence":"additional","affiliation":[{"name":"Peking University People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Xie","sequence":"additional","affiliation":[{"name":"Peking University People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University People\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3358-181X","authenticated-orcid":true,"given":"Jianfeng","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02se0t636","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Beijing Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ol-51-13-3745-R1","doi-asserted-by":"publisher","first-page":"121711","DOI":"10.1117\/1.JBO.22.12.121711","type":"journal-article","volume":"22","author":"Wang","year":"2017","journal-title":"J. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}