Prostate MRI Super-Resolution using Discrete Residual Diffusion Model.

Prostate cancer (PCa) is one of the most common malignant tumors. High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to create a higher-resolution MRI. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems, called Prostate MRI Super-Resolution using Discrete Residual Diffusion Model (DR-DM). First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables. The backward process optimizes a variant of the variational lower bound, training diffusion models effectively address the mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging, thereby improving the fidelity of the reconstructed images. Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves image quality, resulting in greater clarity and improved diagnostic accuracy for patients.