ArticleThoracic cancer2025
Comparison of Various GAN-Based Bone Suppression Imaging for High-Accurate Markerless Motion Tracking of Lung Tumors in CyberKnife Treatment.
Article in Thoracic cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Stereotactic body radiation therapy (SBRT) is a highly effective treatment for lung cancer; however, challenges arise from tumor motion induced by respiration. The CyberKnife system, incorporating both fiducial-based and fiducial-free tracking modalities, aims to mitigate these challenges, yet tumor recognition can be compromised by overlapping bone structures. This study introduces a novel bone suppression imaging technique for kilovolt X-ray imaging using generative adversarial networks (GANs) to enhance tumor tracking in SBRT by reducing interference from bony structures. Computed tomography (CT) images, both with and without bone structures, were generated using a four-dimensional extended cardiac-torso phantom (XCAT phantom) across 56 cases. X-ray projections were captured from left and right oblique 45° angles and divided into nine segments, producing 1120 images. These images were processed through six pre-trained GAN models-CycleGAN, DualGAN, CUT, FastCUT, DCLGAN, and SimDCL-yielding bone-suppressed images on the XCAT phantom (BSI
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