Regularizing INR with diffusion prior self-supervised 3D reconstruction of neutron computed tomography data

TL;DR

This paper introduces DINR, a diffusion-regularized implicit neural representation framework for neutron CT reconstruction, significantly improving quality from sparse data by leveraging synthetic pretraining and diffusion priors.

Contribution

The paper proposes a novel DINR framework that combines diffusion priors with INRs for high-quality sparse-view neutron CT reconstruction, outperforming traditional methods.

Findings

Superior PSNR and SSIM compared to state-of-the-art methods

Effective in reconstructing microstructures from extremely sparse data

Reduces artifacts in neutron CT images

Abstract

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.