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Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
May 2, 2024, 4:45 a.m. | Zeyu Tang, Xiaodan Xing, Guang Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring appropriate training data for these Super-Resolution (SR) models is challenging. Previous SR research has simulated thick-slice CT images from thin-slice CT images to create training pairs. However, these methods either rely on simplistic interpolation techniques that lack realism or sinogram reconstruction, which require the release of …
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