3-D Computed Laminography Based on a Sequential Regularization

Accurate reconstruction of computed laminography (CL) remains challenging due to incomplete projections causing inter-slice aliasing and blurring. In this paper, we propose a novel 3-D CL reconstruction model named SART-SR, which extends traditional single-term regularization methods into a sequential regularization (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 develop an alternating minimization algorithm based on the Split-Bregman method and gradient descent. The results on simulated MPCB and flange plate phantoms demonstrate that SART-SR notably outperforms competing iterative methods, including simultaneous algebraic reconstruction technique (SART), in preserving edges, suppressing inter-slice aliasing, and reducing noise. The code is publicly available at https://github.com/YuhangLiu98/SART-SR.