Deep Learning-based Flexible Echo Time Dual-Echo Water-Fat Separation

We designed a deep learning-based dual-echo water-fat separation method with capability to support flexible echo times. A densely connected hierarchical network was employed, where input included dual-echo images and echo times, and ground truth images were produced using the projected power method. The model was trained and tested using 78 contrast enhanced image sets acquired with optimal echo times, and further validated on 15 non-contrast enhanced image sets obtained with different imaging parameter values. The proposed water-fat separation method has demonstrated high accuracy when dual-echo images were acquired with optimal or non-optimal echo times.