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 https://github.com/xmed-lab/DiffCMR.

翻译：从欠采样k空间数据中进行磁共振成像（MRI）重建可以加速MRI扫描过程并减少患者不适。该重建问题通常被表述为一项去噪任务，用于去除欠采样MRI图像切片中的噪声。尽管先前基于GAN的方法在图像去噪中取得了良好性能，但它们难以训练，且需要仔细调整超参数。本文提出了一种新颖的MRI去噪框架DiffCMR，通过利用条件去噪扩散概率模型。具体而言，DiffCMR从欠采样MRI图像切片中感知条件信号，并生成其对应的全采样MRI图像切片。在推理过程中，我们采用多轮集成策略来稳定性能。我们在MICCAI 2023心脏MRI重建挑战赛（CMRxRecon）数据集上使用电影重建和T1/T2映射任务验证了DiffCMR。结果表明，我们的方法达到了最先进的性能，显著超越了先前方法。代码可在https://github.com/xmed-lab/DiffCMR获取。