Super Resolution (SR) plays a critical role in computer vision, particularly in medical imaging, where hardware and acquisition time constraints often result in low spatial and temporal resolution. While diffusion models have been applied for both spatial and temporal SR, few studies have explored their use for joint spatial and temporal SR, particularly in medical imaging. In this work, we address this gap by proposing to use a Latent Diffusion Model (LDM) combined with a Vector Quantised GAN (VQGAN)-based encoder-decoder architecture for joint super resolution. We frame SR as an image denoising problem, focusing on improving both spatial and temporal resolution in medical images. Using the cardiac MRI dataset from the Data Science Bowl Cardiac Challenge, consisting of 2D cine images with a spatial resolution of 256x256 and 8-14 slices per time-step, we demonstrate the effectiveness of our approach. Our LDM model achieves Peak Signal to Noise Ratio (PSNR) of 30.37, Structural Similarity Index (SSIM) of 0.7580, and Learned Perceptual Image Patch Similarity (LPIPS) of 0.2756, outperforming simple baseline method by 5% in PSNR, 6.5% in SSIM, 39% in LPIPS. Our LDM model generates images with high fidelity and perceptual quality with 15 diffusion steps. These results suggest that LDMs hold promise for advancing super resolution in medical imaging, potentially enhancing diagnostic accuracy and patient outcomes. Code link is also shared.

本研究解决了医疗影像领域时空超分辨率的不足，通过提出将潜在扩散模型（LDM）与基于矢量量化生成对抗网络（VQGAN）编码解码架构相结合的方法，实现了空间和时间的联合超分辨率。研究结果表明，该方法在心脏MRI数据集上的表现显著提升，能够提高医疗图像的诊断准确性和患者预后。

医疗MRI图像中的时空超分辨率与潜在扩散模型