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arXiv · 2502.09793

Noise Controlled CT Super-Resolution with Conditional Diffusion Model

Yuang Wang·Siyeop Yoon·Rui Hu·Baihui Yu·Duhgoon Lee·Rajiv Gupta·Li Zhang·Zhiqiang Chen·Dufan Wu
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Abstract

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.

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Yuang Wang, Siyeop Yoon, Rui Hu, Baihui Yu, Duhgoon Lee, Rajiv Gupta, Li Zhang, Zhiqiang Chen, Dufan Wu. 2025-02-13. Noise Controlled CT Super-Resolution with Conditional Diffusion Model. https://arxiv.org/abs/2502.09793

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