Estimación de PDFF usando una red neuronal con múltiples decodificadores para la separación de agua-grasa con un número reducido de ecos

Abstract

Objective To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based MultiDecoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes. Methods The proposed MDWF-Net and a U-Net model were independently trained using the frst 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (signifcance level: .05). A 6-echo graph cut was considered ground truth. Results Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope=0.94, R2=0.97) than U-Net (regression slope=0.86, R2=0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical diference between graph cuts and U-Net (p<.05), unlike MDWF-Net (p=.53). Conclusion MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times. Clinical relevance statement We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a signifcant reduction in MR scan time by reducing the number of echoes required by 50%. Key Points • Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a signifcant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no signifcant diferences in PDFF estimation with respect to the reference technique.