Projection correction hybrid-domain CT reconstruction algorithm for 18650 power batteries

Abstract

In industrial computed tomography for defect detection in 18650 lithium-ion batteries, streak-like artifacts caused by sparse-view projection sampling severely hinder the accurate identification of subtle structural defects. This paper proposes a hybrid-domain CT reconstruction algorithm with projection inpainting based on a joint CNN-Transformer architecture. The proposed method integrates projection-domain restoration with image-domain optimization to construct an end-to-end dual-domain collaborative reconstruction framework. Specifically, a hybrid-domain consistent restoration module is designed to leverage filtered back-projection priors to guide the convolutional network in performing an initial completion of sparse-view projection data. In addition, a multi-level Transformer structure is introduced to model global correlations across projection views, thereby accurately correcting projection deviations caused by sparse sampling and noise. The overall framework enables an accurate mapping from undersampled sinograms to high-fidelity CT images. Simulation and experimental results demonstrate that the proposed method effectively suppresses artifacts under sparse-view projection sampling condition and outperforms present methods in key metrics such as RMSE, PSNR, and SSIM. In particular, it shows superior performance in edge preservation and structural recovery for pixel-level defects, highlighting its strong potential for high-performance industrial CT defect inspection.