Industrial CT Truncated Projection Data Reconstruction System Based on Deep Learning

Industrial computed tomography (CT) technology is the core means of industrial non-destructive testing, and the accuracy and efficiency of its data reconstruction algorithm directly affect the credibility of the detection results. However, the presence of truncated projection data results in traditional reconstruction methods facing issues such as artifacts, low resolution, and high computational complexity. This article proposes an artificial intelligence based industrial CT truncated projection data reconstruction system, which combines deep learning technology with high response dataset construction methods to optimize data preprocessing, feature extraction, and model training processes. Experiments have shown that the system outperforms traditional iterative algorithms in both reconstruction accuracy and computational efficiency, and has the ability to dynamically adapt to complex industrial scenarios.