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Abstract #0810

Liver Proton Density Fat Fraction (PDFF) at 0.55T Using Locally Low-Rank Deep Learning (LLR-DL) Reconstruction

Majd Helo1,2, Marcel Dominik Nickel2, Stephan Kannengiesser2, and Thomas Kuestner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany, 2Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany

Synopsis

Keywords: Quantitative Imaging, Low-Field MRI, PDFF, Low-Field, Liver, Sparse and Low-Rank

Motivation: New medications like Resmetirom have increased the demand to assess fatty liver diseases with Proton Density Fat Fraction (PDFF) MRI. Imaging at low-field can help meet this demand but is challenging due to low signal-to-noise ratio (SNR).

Goal(s): Provide a framework to reconstruct high SNR images and precise PDFF and R2* maps.

Approach: Develop and compare multi-echo Dixon sequence with joint deep learning (DL)-based reconstruction of multiple contrasts at 0.55T with the established approach at 1.5T.

Results: High SNR images reflected in the PDFF precision reaching a reproducibility coefficient of 0.23% and 1.52%, compared to 3.14% and 4.48% achieved using conventional parallel imaging.

Impact: Multi-echo Dixon acquisitions with short echo times combined with iterative DL-based reconstruction using locally low-rank regularization yields high SNR images. This approach enables precise PDFF quantification and provides high-SNR R2* maps at 0.55T.

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Keywords