研究人员开发了PhyDiCT，一种从有限X射线投影重建3D计算机断层扫描（CT）图像的新型框架。这种无需训练的方法结合了一个基于物理的可微分正向模型（源于比尔-兰伯特定律）和一个充当强先验的文本条件扩散模型。该系统使用Split Gibbs采样来优化投影保真度和先验一致性，并增加了测试时精炼步骤以增强真实感。评估表明，PhyDiCT的性能优于现有的即插即用扩散和完全训练的重建方法，在SSIM方面提高了7.5%。 AI
影响 该方法可以通过从更少的X射线输入实现更高质量的CT扫描来改善医学成像。
排序理由 该集群描述了一篇详细介绍新型图像重建方法的最新研究论文。
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Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free framework that integrates a differentiable Physics-based forward model, grounded in …
arXiv:2610.11547v1 Announce Type: new Abstract: NeRF and Gaussian splatting methods have been successfully applied on X-ray scenes where the views are too sparse for 3D reconstruction via classical methods. Ultra-sparse scenes with 10 or fewer views such as those with high-rate o…
arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free framework that integrates a differen…