用扩散模型先验正则化隐式神经表示，实现稀疏视角中子断层成像的高质量重建。
Regularizing INR with diffusion prior self-supervised 3D reconstruction of neutron computed tomography data
- 将扩散模型作为先验，正则化隐式神经表示以提升重建质量。
- 在极低视角数据下，PSNR和SSIM均显著优于现有方法。
- 适用于混凝土微观结构等极端数据受限场景的高精度重建。
生成式扩散先验近年来在逆问题求解中取得显著进展，具备适应分布外数据推理的能力。同时，隐式神经表示（INRs）作为快速轻量的逆成像求解器，易于与传统逆问题公式结合。本文提出一种用于正则化INRs的扩散型断层成像（CT）反演框架——扩散隐式神经表示（DINR），旨在实现稀疏视角中子断层扫描的高质量重建。DINR仅在合成数据上预训练，评估于模拟及实验获取的混凝土微结构观测数据，传统重建方法在视图数减少时性能大幅下降。本方法显著降低重建伪影，提升PSNR与SSIM，在极端数据限制下仍可实现准确的微结构表征，优于当前最先进的稀疏视图重建技术。
原文摘要 · Abstract (English)
Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.
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