MR Elastography Image Reconstruction using Spatio-Temporal Neural Networks-based Regularization

Motivation: MR Elastography (MRE) is a quantitative, noninvasive method to map viscoelastic properties in tissue. We propose a method of advanced image reconstruction to increase resolution and accuracy for fast MRE. Goal(s): We aim to reduce scan time and, eventually, enable real-time MRE. Approach: To overcome artifacts resulting from the accelerated data acquisition, we employ a data-driven regularization method. Our approach utilizes a physics-informed convolutional neural network (CNN) that exploits spatio-temporal correlation among the images. Results: We show that the employed spatio-temporal approach can improve the image reconstruction performance and further outperforms iterative SENSE reconstructions and standard 2D U-Net approaches. Impact: Our approach allows for the accurate estimation of elastograms from strongly undersampled data, thus allowing a highly reduced scan time. These improvements will eventually benefit clinical practice, making MRE an even more powerful imaging tool.