Prior Image-Guided Adaptive-Weighted Relative Total Variation for Sparse-View Computed Laminography of Plate-like Objects

X-ray computed laminography (CL) is a promising nondestructive testing technique for plate-type objects and is of great importance in 3D imaging. Nevertheless, its scanning geometry results in a lack of projection data along in-plane directions, causing severe inter-slice aliasing and cone-beam artifacts, especially under sparse-view sampling. To address this challenge, a prior image-guided adaptive-weighted relative total variation (PiAwRTV) algorithm is proposed for sparse-view CL. Based on relative total variation (RTV), PiAwRTV leverages structural information from a high-quality prior image to guide image reconstruction and introduces weights that vary with local image gradients. The reconstruction model incorporates 2D PiAwRTV in the horizontal direction to perform edge-preserving smoothing and 1D PiAwRTV in the vertical direction to suppress inter-slice blurring. An alternating minimization strategy is employed to decompose this optimization problem into three subproblems for iterative solution. Experimental results demonstrate that the proposed algorithm reconstructs key structural features while reducing cone-beam artifacts, significantly improving the imaging quality of sparse-view CL.