用条件扩散模型实现降噪超分辨率，提升CT图像质量。
Noise Controlled CT Super-Resolution with Conditional Diffusion Model
- 基于条件扩散模型，融合仿真与真实数据训练。
- 在真实CT数据上验证，有效提升分辨率且抑制噪声放大。
- 适合临床CT图像增强，尤其关注噪声控制的应用场景。
提高CT图像的空间分辨率是一项有意义但具有挑战性的任务，常伴随噪声放大的问题。本文提出一种基于条件扩散模型的噪声可控CT超分辨率框架。模型在混合数据集上训练，结合了噪声匹配的仿真数据与真实数据中的细节分割信息。通过真实CT图像的实验验证，所提框架表现出良好的有效性，展现出在实际CT成像应用中的潜力。
原文摘要 · Abstract (English)
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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