Performing magnetic resonance imaging (MRI) reconstruction from under-sampled
k-space data can accelerate the procedure to acquire MRI scans and reduce
patients' discomfort. The reconstruction problem is usually formulated as a
denoising task that removes the noise in under-sampled MRI image slices.
Although previous GAN-based methods have achieved good performance in image
denoising, they are difficult to train and require careful tuning of
hyperparameters. In this paper, we propose a novel MRI denoising framework
DiffCMR by leveraging conditional denoising diffusion probabilistic models.
Specifically, DiffCMR perceives conditioning signals from the under-sampled MRI
image slice and generates its corresponding fully-sampled MRI image slice.
During inference, we adopt a multi-round ensembling strategy to stabilize the
performance. We validate DiffCMR with cine reconstruction and T1/T2 mapping
tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset.
Results show that our method achieves state-of-the-art performance, exceeding
previous methods by a significant margin. Code is available at
this https URL

本研究提出了一种基于条件去噪扩散概率模型的新型磁共振成像 (MRI) 图像去噪框架 DiffCMR，通过感知来自欠采样 MRI 图像切片的条件信号，并生成相应的全采样 MRI 图像切片。经过多轮合成策略的推断，验证了 DiffCMR 在 MICCAI 2023 心脏 MRI 重建挑战数据集上的心动影像重建和 T1/T2 映射任务，并显示出超越先前方法的最先进性能。