Application of a 3D-ResNet Model Based on Dual-echo Dixon Fat-only Images for Grading Diagnosis of Fatty Liver Disease

LI Yaguang; HOU Ruihong; LI Xiang; MU Jiankui; WANG Qiang; WANG Yong; LEI Licun

doi:10.15953/j.ctta.2025.262

Abstract：Objective:Using hepatic magnetic resonance imaging proton density fat fraction(MRI-PDFF)as the reference standard,we constructed a 3D-ResNet model based on dual-echo Dixon fat-only images to explore the value of deep learning in the grading of fatty liver disease.Materials and Methods:Overall,132 participants were prospectively enrolled to undergo both dual-echo and multi-echo Dixon liver MRI.Among them,94 participants were diagnosed with fatty liver disease by MRI-PDFF,and 38 participants were without fatty liver disease.The inter-sequence agreement of fat-only images between the two Dixon protocols was evaluated across hepatic lobes to determine the optimal lobe for region of interest(ROI)placement.Circular ROIs were drawn on dual-echo Dixon fat-only images,and the corresponding PDFF values from multi-echo Dixon served as the grading reference.In total,3,104 ROIs were included:Grade 0(normal),1,026;Grade 1(mild),1,042;and Grade 2/3(moderate-to-severe),1,036.A 3D-ResNet model was developed using dual-echo Dixon fat-only images with hyperparameter optimization and overfitting prevention.The model performance was assessed on the test set via receiver operating characteristic(ROC)and precision-recall(P-R)curves.The values of area under the curve(AUC),average precision(AP),accuracy,precision,recall,specificity,and F1-score were calculated.Results:The following were achieved by the 3D-ResNet model based on dual-echo Dixon fat-only images:a macro-average AUC of 0.971,mean AP of 0.943,accuracy of 88.60%,and weighted F1-score of 88.59%for distinguishing normal,mild,and moderate-to-severe fatty liver.Conclusion:The 3D-ResNet model built on dual-echo Dixon fat-only images demonstrates high accuracy and robustness for grading fatty liver disease and can serve as an effective screening tool for assessing hepatic steatosis.