CCDM: Continuous-Time Conditional Diffusion Model for Blind CMRI Super-Resolution

Diffusion probabilistic models have effectively addressed the ill-posed nature of cardiac magnetic resonance imaging (CMRI) super-resolution (SR) by learning high-resolution image distributions from low-resolution inputs. However, the iterative sampling process in these models often suffers from slow inference speeds, as well as limitations in the quality and structural consistency of the generated images. To address these challenges, we propose a continuous-time conditional diffusion model (CCDM) for blind CMRI SR. Specifically, we propose a continuous-time conditional diffusion module that reduces the time consumption of the diffusion probability model by maintaining the mean and variance of the data in the forward process. Meanwhile, we design a cascaded residual attention network as a feature extractor to enhance the model’s discriminative power and feature representation capabilities. To further elevate image fidelity, we propose an image quality loss module that integrates a score matching loss, significantly improving detail reconstruction and overall perceptual quality. Furthermore, we develop a hybrid score predictor that approximates the conditional score function via a hybrid parameterized denoising network, facilitating efficient CMRI generation through probability flow sampling. Extensive experimental results demonstrate that compared to existing diffusion model-based SR methods, our CCDM achieves significant improvements in SR quality while substantially reducing time consumption.