Deep Incremental Angle Refinement Model for Limited-Angle CT Reconstruction - A Case Study on Concrete Specimens

Industrial computed tomography (CT) is widely utilized for non-destructive testing and quality control in various industries. However, a common challenge in industrial CT is the presence of artifacts caused by limited angle tomography, where the object cannot be rotated fully due to geometric constraints or time limitations. To eliminate the artifacts, we propose a novel framework based on diffusion model: Deep Incremental Angle Refinement Model (DI-ARM). Our approach leverages the characteristics of CT projection by using reconstructed data of different limited angles as intermediate steps in the training process, replacing the traditional diffusion model of adding random Gaussian noise. This approach ensures data consistency in training process, mitigating the instability caused by sampling randomness of diffusion models. Furthermore, our method requires fewer steps compared to conventional diffusion models, significantly reducing computational resource consumption.