{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T22:19:39Z","timestamp":1771971579813,"version":"3.50.1"},"reference-count":44,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"crossref","award":["R01EB031577"],"award-info":[{"award-number":["R01EB031577"]}],"id":[{"id":"10.13039\/100000009","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":[[2025,2,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Lung tumors can be obscured in x-rays, preventing accurate and robust localization. To improve lung conspicuity for image-guided procedures, we isolate the lungs in the anterior-posterior (AP) x-rays using a lung extraction network (LeX-net) that virtually removes overlapping thoracic structures, including ribs, diaphragm, liver, heart, and trachea. <jats:italic>Approach.<\/jats:italic> 73\u2009965 thoracic 3DCTs and 106 thoracic 4DCTs were included. The 3D lung volume was extracted using an open-source lung volume segmentation model. AP digitally reconstructed radiographs (DRRs) of the full anatomy CT and extracted lungs were computed as the input and reference to train a network (LeX-net) to generate lung-extracted DRRs (LeX-net DRRs) from full anatomy DRRs, which adopted a Swin-UNet model with conditional GAN. Subsequently, the trained LeX-net on 3DCT was applied to 4DCT-derived DRRs. Lung tumor tracking was then performed on DRRs using a template-matching method on a holdoff 4DCT test set of 79 patients whose gross tumor volumes were smaller than 20 cm<jats:sup>3<\/jats:sup>. <jats:italic>Main results<\/jats:italic>. LeX-net successfully isolated the lungs in DRRs, achieving an SSIM of 0.9581 \u00b1 0.0151 and a PSNR of 30.78 \u00b1 2.50 on the testing set of 3DCT-derived DRRs. Its performance declined slightly when applied to the 4DCT but maintained useable lung-only 2D views. On the challenging test set including cases of organ overlap, high tumor mobility, and small tumor size, the individual tumor tracking error for LeX-net DRRs was 0.97 \u00b1 0.86 mm, significantly lower than that of 3.13 \u00b1 5.82 mm using the full anatomy DRRs. LeX-net improved success rates of using 5 mm, 3 mm, and 1 mm tracking windows from 88.1%, 80.0%, and 58.7% to 98.1%, 94.2%, and 73.8%, respectively. <jats:italic>Significance<\/jats:italic>. LeX-net removes overlapping anatomies and enhances visualization of the lungs in x-rays. The model trained using 3DCTs is generalizable to 4DCT-derived DRRs, achieving significantly improved tumor tracking outcome.<\/jats:p>","DOI":"10.1088\/1361-6560\/ada719","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T23:00:47Z","timestamp":1736290847000},"page":"035006","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Real-time lung extraction from synthesized x-rays improves pulmonary image-guided radiotherapy"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6656-1837","authenticated-orcid":true,"given":"Xinyi","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4819-8314","authenticated-orcid":true,"given":"Katelyn","family":"Hasse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5587-5275","authenticated-orcid":true,"given":"Di","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7155-9552","authenticated-orcid":true,"given":"Qifan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martina","family":"Descovich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3400-7684","authenticated-orcid":true,"given":"Dan","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6696-5409","authenticated-orcid":true,"given":"Ke","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"pmbada719bib1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000032899","article-title":"Non-small cell lung cancer (NSCLC): a review of risk factors, diagnosis, and treatment","volume":"102","author":"Alduais","year":"2023","journal-title":"Medicine"},{"key":"pmbada719bib2","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/1748-717X-9-210","article-title":"Stereotactic body radiation therapy (SBRT) for lung cancer patients previously treated with conventional radiotherapy: a review","volume":"9","author":"Amini","year":"2014","journal-title":"Radiat. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-10-28","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-07","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-01-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}