CT respiratory motion synthesis using joint supervised and adversarial learning
This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan.PMID:38537289 | DOI:10.1088/1361-6560/ad388a
Source: Physics in Medicine and Biology - Category: Physics Authors: Yi-Heng Cao Vincent Bourbonne Fran çois Lucia Ulrike Schick Julien Bert Vincent Jaouen Dimitris Visvikis Source Type: research
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