用扩散模型提升低剂量CT图像质量，实现高保真重建。
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
- 基于真实退化模型生成低质CT图像，结合文本描述控制生成过程。
- 在多个公开数据集上超越现有方法，重建图像保持解剖细节。
- 适合医学影像领域研究者，尤其关注低剂量成像与生成模型应用。
高分辨率计算机断层扫描（CT）对医疗诊断至关重要，但会增加辐射暴露，造成图像质量与患者安全之间的权衡。尽管深度学习在CT超分辨率方面展现出潜力，但仍面临复杂退化和有限医疗训练数据的挑战。大型预训练扩散模型（如Stable Diffusion）在多种视觉任务中表现出卓越的细节合成能力。受此启发，我们提出一种新框架，将Stable Diffusion适配于CT盲超分辨率。通过实用的退化模型合成真实低质量图像，并利用预训练视觉-语言模型生成对应文本描述。随后，采用专有控制策略，基于低分辨率输入和生成的文本描述进行超分辨率重建。大量实验表明，该方法优于现有方法，在减少辐射剂量的同时实现高质量图像重建。代码将公开共享。
原文摘要 · Abstract (English)
High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical trade-off between image quality and patient safety. While deep learning methods have shown promise in CT super-resolution, they face challenges with complex degradations and limited medical training data. Meanwhile, large-scale pre-trained diffusion models, particularly Stable Diffusion, have demonstrated remarkable capabilities in synthesizing fine details across various vision tasks. Motivated by this, we propose a novel framework that adapts Stable Diffusion for CT blind super-resolution. We employ a practical degradation model to synthesize realistic low-quality images and leverage a pre-trained vision-language model to generate corresponding descriptions. Subsequently, we perform super-resolution using Stable Diffusion with a specialized controlling strategy, conditioned on both low-resolution inputs and the generated text descriptions. Extensive experiments show that our method outperforms existing approaches, demonstrating its potential for achieving high-quality CT imaging at reduced radiation doses. Our code will be made publicly available.
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