{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:21:09Z","timestamp":1789726869203,"version":"4.0.1"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T00:00:00Z","timestamp":1737849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3107200"],"award-info":[{"award-number":["2023YFC3107200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["52376160"],"award-info":[{"award-number":["52376160"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["52006137"],"award-info":[{"award-number":["52006137"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024C027"],"award-info":[{"award-number":["2024C027"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2023YFC3107200"],"award-info":[{"award-number":["2023YFC3107200"]}]},{"name":"National Natural Science Foundation of China","award":["52376160"],"award-info":[{"award-number":["52376160"]}]},{"name":"National Natural Science Foundation of China","award":["52006137"],"award-info":[{"award-number":["52006137"]}]},{"name":"National Natural Science Foundation of China","award":["2024C027"],"award-info":[{"award-number":["2024C027"]}]},{"name":"Chongqing medical scientific research project","award":["2023YFC3107200"],"award-info":[{"award-number":["2023YFC3107200"]}]},{"name":"Chongqing medical scientific research project","award":["52376160"],"award-info":[{"award-number":["52376160"]}]},{"name":"Chongqing medical scientific research project","award":["52006137"],"award-info":[{"award-number":["52006137"]}]},{"name":"Chongqing medical scientific research project","award":["2024C027"],"award-info":[{"award-number":["2024C027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>In image-guided surgery (IGS) practice, combining intraoperative 2D X-ray images with preoperative 3D X-ray images from computed tomography (CT) enables the rapid and accurate localization of lesions, which allows for a more minimally invasive and efficient surgery, and also reduces the risk of secondary injuries to nerves and vessels. Conventional optimization-based methods for 2D X-ray and 3D CT matching are limited in speed and precision due to non-convex optimization spaces and a constrained searching range. Recently, deep learning (DL) approaches have demonstrated remarkable proficiency in solving complex nonlinear 2D\u20133D registration. In this paper, a fast and robust DL-based registration method is proposed that takes an intraoperative 2D X-ray image as input, compares it with the preoperative 3D CT, and outputs their relative pose in x, y, z and pitch, yaw, roll. The method employs a dual-channel Swin transformer feature extractor equipped with attention mechanisms and feature pyramid to facilitate the correlation between features of the 2D X-ray and anatomical pose of CT. Tests on three different regions of interest acquired from open-source datasets show that our method can achieve high pose estimation accuracy (mean rotation and translation error of 0.142\u00b0 and 0.362 mm, respectively) in a short time (0.02 s). Robustness tests indicate that our proposed method can maintain zero registration failures across varying levels of noise. This generalizable learning-based 2D (X-ray) and 3D (CT) registration algorithm owns promising applications in surgical navigation, targeted radiotherapy, and other clinical operations, with substantial potential for enhancing the accuracy and efficiency of image-guided surgery.<\/jats:p>","DOI":"10.3390\/bioengineering12020114","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T03:54:27Z","timestamp":1737950067000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Robust Method for Real Time Intraoperative 2D and Preoperative 3D X-Ray Image Registration Based on an Enhanced Swin Transformer Framework"],"prefix":"10.3390","volume":"12","author":[{"given":"Wentao","family":"Ye","sequence":"first","affiliation":[{"name":"China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghong","family":"Wu","sequence":"additional","affiliation":[{"name":"Xinqiao Hospital, Chongqing 400037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3103-6909","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyang","family":"Sun","sequence":"additional","affiliation":[{"name":"China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Dong","sequence":"additional","affiliation":[{"name":"China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuogui","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Changhai Hospital, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1146\/annurev-bioeng-070909-105249","article-title":"Image-guided interventions: Technology review and clinical applications","volume":"12","author":"Cleary","year":"2010","journal-title":"Annu. 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