Cpi-awHOTV: A CAD prior improved adaptive-weighted high order TV algorithm for orthogonal translation CL.

Background: Orthogonal translation computed laminography (OTCL) has great potential for tiny fault detection in laminated structure thin-plate parts. It offers a larger magnification ratio but generates limited projection data, which would result in aliasing artifacts in the reconstructed image.

Objective: One way to minimize these artifacts is to use prior information, such as the piecewise constant property and prior image information. This work was inspired by the adaptive-weighted high order total variation (awHOTV) model, which is known for its ability to protect edge and detail information. Meanwhile, the laminated structure thin-plate parts are printed using computer-aided design (CAD) images, which provide structural information.

Methods: To create a reliable CAD information beforehand, we adopted a two-in-one estimation method. Therefore, combining the CAD information with the awHOTV model, we propose an improved adaptive weighted higher-order TV (Cpi-awHOTV) model based on the CAD prior and use the adaptive steepest descent projection onto convex set (ASD-POCS) algorithm to solve the imaging model.

Results: To evaluate the performance of our algorithm, we compared it with existing filtered back projection (FBP), simultaneous algebraic reconstruction technique (SART), total variation (TV), adaptive-weighted TV (awTV), and high order TV (HOTV)algorithms on phantom1 and phantom2 with various scanning angle ranges. Additionally, we used the phantom2 as the CAD prior in real data experiments. The results show that, the Cpi-awHOTV algorithm can obtain high-quality reconstructed images and better quantitative evaluation indicators.

Conclusions: Visual inspection and quantitative analysis of reconstructed images demonstrate that the Cpi-awHOTV algorithm effectively protects edge information, and reduces aliasing artifacts due to interference from adjacent slice structures.