Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising Diffusion Models for Denoising Diffusion MRI (DDM$^2$), a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our three-stage framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation. During inference, we represent input noisy measurements as a sample from an intermediate posterior distribution within the diffusion Markov chain. We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM$^2$ demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics.

翻译：磁共振成像（MRI）是一种常见且可挽救生命的医学成像技术。然而，获取高信噪比的MRI扫描需要较长的扫描时间，从而导致成本增加、患者不适以及吞吐量下降。因此，对MRI扫描（尤其是信噪比严重受限的扩散MRI子类型）进行降噪具有重大意义。尽管先前多数MRI降噪方法本质上是监督式的，但为多种解剖结构、MRI扫描仪和扫描参数获取监督训练数据集被证明不切实际。本文提出了一种用于扩散MRI降噪的降噪扩散模型（DDM$^2$），这是一种利用扩散降噪生成模型进行MRI降噪的自监督降噪方法。我们的三阶段框架将基于统计学的降噪理论融入扩散模型，并通过条件生成实现降噪。在推理过程中，我们将输入含噪测量值表示为扩散马尔可夫链中后验分布的中间采样。我们在4个真实活体扩散MRI数据集上进行实验，结果表明DDM$^2$在临床相关的视觉定性及定量指标上展现出卓越的降噪性能。