用扩散模型同时提升医学MRI的时空分辨率，效果优于传统方法。
Temporal and Spatial Super Resolution with Latent Diffusion Model in Medical MRI images
- 将超分任务转化为潜空间去噪，结合VQGAN编码器实现时空联合重建。
- 在心脏MRI数据上达到PSNR 30.37、SSIM 0.7580，优于基线5%~39%。
- 仅需15步扩散即可生成高保真图像，适合临床诊断应用。
超分辨率（SR）在计算机视觉中至关重要，尤其在医学成像领域，由于硬件和采集时间限制，常导致空间和时间分辨率较低。尽管扩散模型已被用于空间和时间超分辨率，但针对医学影像中联合时空超分辨率的研究仍较少。本文提出一种基于潜空间扩散模型（LDM）与VQGAN编码器-解码器架构的方法，将超分辨率建模为图像去噪问题，以同时提升医学图像的空间与时间分辨率。在数据科学碗心脏挑战赛提供的2D电影型心肌磁共振数据集上进行测试，该数据集包含256x256像素的空间分辨率，每时间步8-14个切片。实验结果表明，所提LDM模型在峰值信噪比（PSNR）达30.37，结构相似性指数（SSIM）为0.7580，感知图像块相似性（LPIPS）为0.2756，相较简单基线方法，分别提升5%、6.5%和39%。模型在仅15次扩散步骤下即生成高保真度和感知质量的图像，验证了潜空间扩散模型在医学超分辨率中的潜力，有望提升诊断准确性与患者预后。代码已公开。
原文摘要 · Abstract (English)
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.
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