Meeting Banner
Abstract #4429

Water-Fat Separation from Dual-Echo Dixon Imaging Using Deep Learning

Yan Wu1, Marc Alley 1, Keshav Datta1, Zhitao Li 1, Christopher Sandino 1, Zhifei Wen2, Michael Lustig3, John Pauly1, and Shreyas Vasanawala1
1Stanford University, Stanford, CA, United States, 2Hoag Hospital, Newport Beach, CA, United States, 3University of California, Berkeley, Berkeley, CA, United States


We design a data-driven method to generate water/fat images from dual-echo complex Dixon images, aimed at near-instant water-fat separation with high robustness. A hierarchical convolutional neural network is employed, where ground truth images are obtained using a binary quadratic optimization approach. With IRB approval and informed consent, 9281 image sets are collected from 30 pediatric patients to train and test networks, with the application of six-fold cross validation. In addition to high fidelity and significantly reduced processing time, the predicted images are superior to the ground truth in mitigation of water/fat swaps and correction of artifacts introduced by metallic implants.

This abstract and the presentation materials are available to members only; a login is required.

Join Here