{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T07:31:03Z","timestamp":1753860663323,"version":"3.41.2"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Lung tumor tracking during stereotactic radiotherapy with the CyberKnife can misrecognize tumor location under conditions where similar patterns exist in the search area. This study aimed to develop a technique for bone signal suppression during kV\u2010x\u2010ray imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Paired CT images were created with or without bony structures using a 4D extended cardiac\u2010torso phantom (XCAT phantom) in 56 cases. Subsequently, 3020 2D x\u2010ray images were generated. Images with bone were input into cycle\u2010consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSI<jats:sub>phantom<\/jats:sub>) were created. They were then compared to images without bone using the structural similarity index measure (SSIM) and peak signal\u2010to\u2010noise ratio (PSNR). Next, 1000 non\u2010simulated treatment images from real cases were input into the training model, and bone\u2010suppressed images of the patient (BSI<jats:sub>patient<\/jats:sub>) were created. Zero means normalized cross correlation (ZNCC) by template matching between each of the actual treatment images and BSI<jats:sub>patient<\/jats:sub> were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>BSI<jats:sub>phantom<\/jats:sub> values were compared to their paired images without bone of the XCAT phantom test data; SSIM and PSNR were 0.90\u00a0\u00b1\u00a00.06 and 24.54\u00a0\u00b1\u00a04.48, respectively. It was visually confirmed that only bone was selectively suppressed without significantly affecting tumor visualization. The ZNCC values of the actual treatment images and BSI<jats:sub>patient<\/jats:sub> were 0.763\u00a0\u00b1\u00a00.136 and 0.773\u00a0\u00b1\u00a00.143, respectively. The BSI<jats:sub>patient<\/jats:sub> showed improved recognition accuracy over the actual treatment images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed bone suppression imaging technique based on CycleGAN improves image recognition, making it possible to achieve highly accurate motion tracking irradiation.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14212","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T22:34:58Z","timestamp":1700606098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cycle\u2010generative adversarial network\u2010based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy"],"prefix":"10.1002","volume":"25","author":[{"given":"Zennosuke","family":"Mochizuki","sequence":"first","affiliation":[{"name":"Department of Radiology 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