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

Maliha Hossain, Haley Duba-Sullivan, Amirkoushyar Ziabari

TL;DR

This paper presents 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.

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.

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

TL;DR

This paper presents 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.

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.
Paper Structure (7 sections, 4 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 7 sections, 4 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Implicit Neural Representations (INRs)
  4. 3D Deep Diffusion Image Prior (DD3IP)
  5. Method
  6. Results
  7. Conclusion

Figures (4)

  • Figure 1: Reference reconstruction for comparison with images in Figures \ref{['fig:synth_results']} and \ref{['fig:real_results']}, as well to calculate metrics in Tables \ref{['tab:synth_comp']} and \ref{['tab:real_comp']}.
  • Figure 2: Reconstruction of synthetic data. The columns and rows in panels a-p are annotated to distinguish different experiments. DINR overall preserves the boundary and texture (pores/microstructure) very well, even for a 4-view ultra-sparse scan.
  • Figure 3: Reconstruction of real data. The columns and rows in panels a-t are annotated to distinguish different experiments. DINR overall preserves the boundary and texture (pores/microstructure) very well, even for a 5-view ultra-sparse scan.
  • Figure 4: PSNR calculated systematically for varying ROI to mask the impact of the background and compare performance in reducing artifacts within microstructure region for a) 5 views, b) 9 views, c) 17 views, and d) 33 views.