3-D Computed Laminography Based on a Sequential Regularization

Abstract

Accurate reconstruction of computed laminography (CL) remains challenging due to incomplete projections causing inter-slice aliasing and blurring. In this article, we propose a novel 3-D CL reconstruction model named simultaneous algebraic reconstruction technique (SART)-sequential regularization (SR), which extends traditional single-term regularization methods into an SR framework specifically designed for anisotropic CL data. Guided by the theory of “visible and invisible boundaries,” this framework decomposes the regularization process into three directional-aware stages: 1) 1-D directional gradient sparsity terms are first applied in the in-slice to enhance reliable edge structures; 2) mild edge-preserving smoothing is applied along the z-direction to reduce aliasing; and 3) a truncated adaptive-weighted total variation (TAwTV) is used for volumetric consistency and streak artifact suppression. To solve the model efficiently, we developed an alternating minimization algorithm based on the split-Bregman (SB) method and gradient descent. The results on simulated multilayer printed circuit board (MPCB) and flange plate phantoms demonstrate that SART-SR notably outperforms competing iterative methods, including SART, in preserving edges, suppressing inter-slice aliasing, and reducing noise. The code is publicly available at https://github.com/YuhangLiu98/SART-SR