Magnetic resonance imaging (MRI) plays an important role in modern medical diagnostic but suffers from prolonged scan time. Current deep learning methods for undersampled MRI reconstruction exhibit good performance in image de-aliasing which can be tailored to the specific kspace undersampling scenario. But it is very troublesome to configure different deep networks when the sampling setting changes. In this work, we propose a deep plug-and-play method for undersampled MRI reconstruction, which effectively adapts to different sampling settings. Specifically, the image de-aliasing prior is first learned by a deep denoiser trained to remove general white Gaussian noise from synthetic data. Then the learned deep denoiser is plugged into an iterative algorithm for image reconstruction. Results on in vivo data demonstrate that the proposed method provides nice and robust accelerated image reconstruction performance under different undersampling patterns and sampling rates, both visually and quantitatively.

翻译：磁共振成像（MRI）在现代医学诊断中具有重要作用，但存在扫描时间较长的问题。当前用于欠采样MRI重建的深度学习方法在图像去伪影方面表现出良好性能，并可针对特定k空间欠采样场景进行定制。然而，当采样设置发生变化时，配置不同的深度网络非常麻烦。在本工作中，我们提出了一种深度即插即用方法用于欠采样MRI重建，能够有效适应不同的采样设置。具体而言，首先通过训练一个深度去噪器来学习图像去伪影先验，该去噪器用于去除合成数据中的通用白高斯噪声。然后将训练好的深度去噪器嵌入到迭代算法中进行图像重建。在体数据上的结果表明，所提方法在不同欠采样模式和采样率下，无论是视觉质量还是定量指标，均能提供稳定且优异的加速图像重建性能。