Abstract
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. But, compared to other clinical imaging modalities (e.g., CT or ultrasound), the data acquisition process for MRI is inherently slow. Furthermore, dynamic applications demand collecting a series of images in quick succession. As a result, reducing acquisition time and improving imaging quality for undersampled datasets have been active areas of research for the last two decades. The combination of parallel imaging and compressive sensing (CS) has been shown to benefit a wide range of MRI applications. More recently, deep learning techniques have been shown to outperform CS methods. Some of these techniques pose the MRI reconstruction as a direct inversion problem and tackle it by training a deep neural network (DNN) to map from the measured Fourier samples and the final image. Considering that the forward model in MRI changes from one dataset to the next, such methods have to be either trained over a large and diverse corpus of data or limited to a specific application, and even then they cannot ensure data consistency. An alternative is to use "plug-and-play" (PnP) algorithms, which iterate image denoising with forward-model based signal recovery. PnP algorithms are an excellent fit for compressive MRI because they decouple image modeling from the forward model, which can change significantly among different scans due to variations in the coil sensitivity maps, sampling patterns, and image resolution. Consequently, with PnP, state-of-the-art image-denoising techniques, such as those based on DNNs, can be directly exploited for compressive MRI image reconstruction. The objective of this article is two-fold: i) to review recent advances in plug-and-play methods, and ii) to discuss their application to compressive MRI image reconstruction.
Abstract (translated)
磁共振成像（MRI）是一种非侵入性诊断工具，无需电离辐射就能提供良好的软组织对比度。但是，与其他临床成像方式（如CT或超声波）相比，MRI的数据采集过程本质上是缓慢的。此外，动态应用需要快速连续地收集一系列图像。因此，在过去20年中，减少采集时间和提高欠采样数据集的成像质量一直是研究的活跃领域。并行成像和压缩传感（CS）的结合已经被证明有利于广泛的MRI应用。最近，深度学习技术已经被证明优于CS方法。其中一些技术将核磁共振成像重建视为一个直接反演问题，并通过训练深神经网络（dnn）从测量的傅立叶样本和最终图像中进行映射来解决。考虑到MRI中的正向模型从一个数据集到下一个数据集发生变化，这些方法必须在大量多样的数据集上进行培训，或者局限于特定的应用程序，即使这样，它们也不能确保数据的一致性。另一种方法是使用“即插即用”（PNP）算法，该算法使用基于前向模型的信号恢复迭代图像去噪。PNP算法非常适合压缩MRI，因为它们将图像建模与前向模型分离开来，由于线圈灵敏度图、采样模式和图像分辨率的变化，前向模型在不同扫描之间可能会发生显著变化。因此，利用PNP，最先进的图像去噪技术，如基于DNN的去噪技术，可以直接用于压缩MRI图像重建。本文的目的是两方面的：一）回顾即插即用方法的最新进展；二）讨论其在压缩MRI图像重建中的应用。
URL
https://arxiv.org/abs/1903.08616
