A study on CT detection image generation based on decompound synthesize method.

Background: Nuclear graphite and carbon components are vital structural elements in the cores of high-temperature gas-cooled reactors(HTGR), serving crucial roles in neutron reflection, moderation, and insulation. The structural integrity and stable operation of these reactors heavily depend on the quality of these components. Helical Computed Tomography (CT) technology provides a method for detecting and intelligently identifying defects within these structures. However, the scarcity of defect datasets limits the performance of deep learning-based detection algorithms due to small sample sizes and class imbalance.

Objective: Given the limited number of actual CT reconstruction images of components and the sparse distribution of defects, this study aims to address the challenges of small sample sizes and class imbalance in defect detection model training by generating approximate CT reconstruction images to augment the defect detection training dataset.

Methods: We propose a novel CT detection image generation algorithm called the Decompound Synthesize Method (DSM), which decomposes the image generation process into three steps: model conversion, background generation, and defect synthesis. First, STL files of various industrial components are converted into voxel data, which undergo forward projection and image reconstruction to obtain corresponding CT images. Next, the Contour-CycleGAN model is employed to generate synthetic images that closely resemble actual CT images. Finally, defects are randomly sampled from an existing defect library and added to the images using the Copy-Adjust-Paste (CAP) method. These steps significantly expand the training dataset with images that closely mimic actual CT reconstructions.

Results: Experimental results validate the effectiveness of the proposed image generation method in defect detection tasks. Datasets generated using DSM exhibit greater similarity to actual CT images, and when combined with original data for training, these datasets enhance defect detection accuracy compared to using only the original images.

Conclusion: The DSM shows promise in addressing the challenges of small sample sizes and class imbalance. Future research can focus on further optimizing the generation algorithm and refining the model structure to enhance the performance and accuracy of defect detection models.