The currently limited quality of accelerated cardiac cine reconstruction may
potentially be improved by the emerging diffusion models, but the clinically
unacceptable long processing time poses a challenge. We aim to develop a
clinically feasible diffusion-model-based reconstruction pipeline to improve
the image quality of cine MRI. A multi-in multi-out diffusion enhancement model
together with fast inference strategies were developed to be used in
conjunction with a reconstruction model. The diffusion reconstruction reduced
spatial and temporal blurring in prospectively undersampled clinical data, as
validated by experts inspection. The 1.5s per video processing time enabled the
approach to be applied in clinical scenarios.

我们旨在开发一种临床可行的基于扩散模型的重建流水线，以改善心脏磁共振成像的图像质量。通过使用多输入多输出的扩散增强模型和快速推理策略，扩散重建减少了临床数据中前瞻性欠采样引起的空间和时间模糊，并得到专家检查的验证。每个视频处理仅需 1.5 秒的时间，使该方法能够在临床场景中应用。