Denoising Diffusion Algorithm for Single Image Inplaine Super-Resolution in CBCT Scans of the Mandible

Deep Learning models are currently the cornerstone of artificial intelligence in medical imaging. The performance of Deep Learning in medical imaging is significantly influenced by the amount and quality of training data. Diffusion models have recently attracted the attention of the computer vision community as they enable photorealistic synthetic image-to-image translation. Previous attempts to use diffusion models for super-resolution imaging have produced satisfactory high-resolution images from low-resolution inputs. However, the drawback is the slow speed of inference, which severely hinders practical applications in medicine. To speed up inference and further improve performance, we propose an accelerated algorithm based on denoising diffusion probability modelling approach for medical image super-resolution. Instead of sampling from pure Gaussian noise, the intermediate distributions of noisy low- and high-resolution images are compared and used to generate super-resolution images. Our proposed algorithm is used to convert low-resolution panoramic X-ray images from Cone-beam Computed Tomography scans of the mandible into high-resolution images for the identification of osteoporosis.