用扩散模型先验提升稀疏视角CT重建质量
A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
- 将扩散生成模型嵌入迭代优化，结合数据与模型先验
- 稀疏视角下重建图像伪影显著减少，细节保持良好
- 适合医学影像重建研究者参考，尤其关注生成先验
从稀疏或有限角度几何采集的投影数据重建X射线CT图像是一项极具挑战性的任务。数据不足常导致图像伪影甚至物体失真。为此，深度生成先验（DGP）框架引入基于扩散的生成模型，与迭代优化算法结合，既保持模型方法的可解释性，又利用神经网络的生成能力。本文针对图像生成、模型结构及优化算法提出改进，结果表明在高度稀疏的几何条件下仍能获得高质量重建，尽管该方向仍需进一步研究。
原文摘要 · Abstract (English)
The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of diffusion-based generative models is combined with an iterative optimization algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.
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