Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography

2026-06-30

AI Summary

This study addresses the challenge of truncated projection data in computed laminography (CL), which arises from limited field-of-view constraints and leads to severe reconstruction artifacts and incomplete 3D structures. To overcome this, the authors propose a pseudo-3D accelerated diffusion model that synergistically combines 2D slice-wise diffusion with 3D model-based iterative reconstruction (MBIR) to extend the effective reconstruction volume while preserving data consistency. The method innovatively incorporates z-direction wavelet regularization, translation-invariant mechanisms, and low-frequency preservation strategies to mitigate inter-slice discontinuities, alongside a novel 3D fast sampling architecture that substantially improves inference efficiency. Experimental results on both simulated and real-world data demonstrate that the proposed approach significantly outperforms existing methods, effectively eliminating truncation artifacts and achieving high-fidelity, continuous 3D reconstructions with markedly enhanced scanning and inference speeds.

Abstract

Computed Laminography (CL) is a key technology for the nondestructive testing of large plate-shaped objects. However, field-of-view (FOV) limitations inevitably lead to truncation of projected data, an ill-posed inverse problem that causes severe reconstruction artifacts. Existing deep learning methods typically rely on 2D architectures that lack rigorous data consistency constraints. Furthermore, they conventionally confine artifact removal strictly to the FOV, discarding potentially recoverable information outside it. To overcome these limitations, we first introduce a comprehensive CL FOV analysis, categorizing the space into data-complete, data-incomplete, and data-free regions. By extending our reconstruction target to encompass the data-incomplete region, we significantly expand the effective imaging range and enhance scanning efficiency. To achieve this, we propose a novel wavelet-optimized pseudo-3D accelerated diffusion model for CL truncation reconstruction (CL-DM). Our method utilizes a standard 2D diffusion model for slice aggregation, combined with a 3D model-based iterative reconstruction (MBIR) method to ensure strict data consistency. To mitigate inter-slice discontinuities, we introduce wavelet regularization along the z-direction, paired with a translation-invariant (TI) mechanism and a low-frequency preservation strategy. Finally, we introduce a 3D fast sampling architecture, significantly accelerating inference speed. Extensive simulations and real-world experiments demonstrate that CL-DM is superior in effectively eliminating truncation artifacts and restoring high-fidelity, continuous 3D structures.

Problem

• Computed Laminography
• truncation artifacts
• field-of-view limitation
• ill-posed inverse problem
• data truncation

Innovation

• diffusion model
• computed laminography
• truncation artifact
• wavelet regularization
• pseudo-3D reconstruction