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Abstract #5179

Reproducible DL-based approach for liver PDFF quantification

Juan Pablo Meneses1,2,3, Cristobal Arrieta1,2, Pablo Irarrazaval1,3,4, Cristian Tejos1,3, Marcelo Andía1,2,5, Carlos Sing Long1,4,6, and Sergio Uribe1,2,5
1Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 2i-Health Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile, 3Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 4Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 5Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile, 6Institute for Mathematical & Computational Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Quantitative Imaging, Convolutional Neural NetworkLiver PDFF is a biomarker correlated with hepatic pathologies. Recently, several Deep Learning (DL) methods have been proposed to accelerate the necessary post-processing to estimate PDFF. However, none of these techniques had been assessed in terms of bias and precision, as suggested by the ISMRM quantitative MR study group. We propose a two-stages framework denoted Variable Echo Times neural Network (VET-Net), which considers multi-echo MR images and their echo times to estimate PDFF. VET-Net showed a bias of -1.35% when tested over a multi-site phantom dataset, and a within-standard deviation of 0.81% over liver MR images with different TEs.

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