用物理约束+分块扩散模型，高效重建医学与科学成像多切片数据
Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
- 分块扩散先验结合物理规律，降低显存占用
- 在MRI和4D-STEM上均优于纯物理或全量重建方法
- 对分布内数据精度高，且对分布外数据泛化能力强
从有限测量数据中准确重建多切片图像对于加速医疗与科学成像的采集过程至关重要。然而，由于问题本身病态性以及高计算与内存需求，该任务仍具挑战。本文提出一种框架，通过将分块扩散先验与基于物理的约束相结合，显著降低每张GPU的内存消耗，同时保持高质量重建效果。在磁共振成像（MRI）和四维扫描透射电镜（4D-STEM）等多种模态上，该方法均优于仅依赖物理模型或完整多切片重建的基线方法。此外，所提方法不仅提升了分布内数据的重建精度，还展现出对分布外数据的强大泛化能力。
原文摘要 · Abstract (English)
Accurate multi-slice reconstruction from limited measurement data is crucial to speed up the acquisition process in medical and scientific imaging. However, it remains challenging due to the ill-posed nature of the problem and the high computational and memory demands. We propose a framework that addresses these challenges by integrating partitioned diffusion priors with physics-based constraints. By doing so, we substantially reduce memory usage per GPU while preserving high reconstruction quality, outperforming both physics-only and full multi-slice reconstruction baselines for different modalities, namely Magnetic Resonance Imaging (MRI) and four-dimensional Scanning Transmission Electron Microscopy (4D-STEM). Additionally, we show that the proposed method improves in-distribution accuracy as well as strong generalization to out-of-distribution datasets.
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