Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.
@article{arxiv.2502.09793,
title = {Noise Controlled CT Super-Resolution with Conditional Diffusion Model},
author = {Yuang Wang and Siyeop Yoon and Rui Hu and Baihui Yu and Duhgoon Lee and Rajiv Gupta and Li Zhang and Zhiqiang Chen and Dufan Wu},
journal= {arXiv preprint arXiv:2502.09793},
year = {2025}
}
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