4D cardiac medical image generation based on a dual U-Net temporal conditional diffusion model

BACKGROUND: Existing medical image generation tasks primarily employ Generative Adversarial Networks (GANs), which perform poorly on datasets with temporal characteristics and suffer from slow generation speed and mode collapse. METHODS: In response to this question, this study puts forward a temporal conditional diffusion model based on a dual U-Net structure, which leverages the dual U-Net to extract rich detail information within a denoising diffusion framework while incorporating temporal information as a condition to guide the generation of 4D cardiac datasets with temporal features. Additionally, a deformation field is utilized to accelerate medical image generation. RESULTS: Experimental results show that compared to existing methods, the proposed approach can generate dynamic scan time frames while maintaining strong continuity and temporal consistency in both transverse and longitudinal spatial dimensions. In addition, the synthesized images are highly similar to those captured in reality. The proposed method effectively preserves anatomical structural details, making it highly suitable for medical image generation tasks.