ArticleJournal of applied clinical medical physics2024
Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy.
Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Comparison of Various GAN-Based Bone Suppression Imaging for High-Accurate Markerless Motion Tracking of Lung Tumors in CyberKnife Treatment.Thoracic cancer · 2025Article
- Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy.Journal of applied clinical medical physics · 2024Article
- Clinical applications of generative artificial intelligence in radiology: image translation, synthesis, and text generation.BJR artificial intelligence · 2024Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLung 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-x-ray imaging.
methodsPaired CT images were created with or without bony structures using a 4D extended cardiac-torso phantom (XCAT phantom) in 56 cases. Subsequently, 3020 2D x-ray images were generated. Images with bone were input into cycle-consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSI
resultsBSI
conclusionsThe proposed bone suppression imaging technique based on CycleGAN improves image recognition, making it possible to achieve highly accurate motion tracking irradiation.
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Registered trials
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