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Abstract #2111

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

Vitaliy Atamaniuk1, Mikołaj Wcisło1, Andrii Pozaruk1, Łukasz Hańczyk2, Marzanna Obrzut1, Bogdan Obrzut1, Krzysztof Gutkowski1, and Marian Cholewa1
1University of Rzeszow, Rzeszow, Poland, 2Clinical Hospital No. 2 in Rzeszow, Rzeszow, Poland

Synopsis

Keywords: Analysis/Processing, Elastography

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.

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