Automated Deep Learning-based Stiffness Quantification in Magnetic Resonance Elastography of the Liver

Motivation: The assessment of liver MRE exams is time-consuming, as is the reconstruction process performed by the scanner. Goal(s): Our objective was to automate the reconstruction and evaluation of stiffness maps, allowing for the calculation of liver stiffness based solely on MRE data, all accomplished within a matter of seconds. Approach: To achieve this, we developed a U-Net-based model combination that takes both magnitude and phase MRE images as input. This model generates stiffness maps and corresponding ROIs while also estimating stiffness within the ROI. Results: The proposed model successfully and accurately estimated liver stiffness, reducing the entire process to a few seconds. Impact: The proposed model can effectively assess liver stiffness using MRE data, substantially decreasing image reconstruction and analysis time to just a few seconds - a crucial advancement for clinical applications.