A Fast Deep Incremental Angle Refinement Model for Limited-Angle CT Reconstruction

Industrial computed tomography (CT) plays a critical role in non-destructive testing and quality control across various industries. However, one of the key challenges in industrial CT lies in artifacts caused by limited-angle tomography, where geometric constraints prevent full rotation of the object. To mitigate these artifacts, we propose an innovative framework leveraging the diffusion model, a state-of-the-art generative model that has garnered significant attention for its powerful generation capabilities. Despite their impressive results, traditional diffusion models typically require upwards of 1,000 intermediate steps to achieve accurate output, leading to substantial computational demands and long inference time, which significantly limit their practical application. Our approach addresses these issues by integrating reconstructed data from different limited angles scans as intermediate steps, substituting the conventional steps of adding random Gaussian noise. This modification significantly reduces the number of intermediate steps necessary for both training and inference, thereby enhancing computational efficiency. Unlike other acceleration techniques that often compromise output quality for speed, our method achieves a significant reduction in computational burden while improving key performance metrics, such as structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). By aligning the training process more closely with real-world data degradation, our approach not only accelerates diffusion models but also enhances the fidelity and quality of the resulting reconstructions, making it a highly promising solution for industrial CT applications.