Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging

• URL: http://arxiv.org/abs/2512.06977v1
• Date: Sun, 07 Dec 2025 20:07:12 GMT
• Title: Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
• Authors: Laurentius Valdy, Richard D. Paul, Alessio Quercia, Zhuo Cao, Xuan Zhao, Hanno Scharr, Arya Bangun,
• Abstract summary: We propose a framework that integrates partitioned diffusion priors with physics-based constraints.<n>By doing so, we substantially reduce memory usage per GPU while preserving high reconstruction quality.<n>We show that the proposed method improves in-distribution accuracy as well as strong generalization to out-of-distribution datasets.
• Score: 2.871453899831962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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