Robust water-fat separation for multi-echo gradient-recalled echo sequence using convolutional neural network

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine
JaeJin Cho, HyunWook Park

Abstract

To accurately separate water and fat signals for bipolar multi-echo gradient-recalled echo sequence using a convolutional neural network (CNN). A CNN architecture was designed and trained using the relationship between multi-echo images from the bipolar multi-echo gradient-recalled echo sequence and artifact-free water-fat-separated images. The artifact-free water-fat-separated images for training the CNN were obtained from multiple signals with different TEs by using iterative decomposition of water and fat with echo asymmetry and the least-squares estimation method, in which multiple signals at different TEs were acquired using a single-echo gradient-recalled echo sequence. We also proposed a data augmentation method using a synthetic field inhomogeneity to generate multi-echo signals, including various bipolar multi-echo gradient-recalled echo artifacts so that the CNN could prevent overfitting and increase the separation accuracy. We trained the CNN using in vivo knee images and tested it using in vivo knee, head, and ankle images. In vivo imaging results showed that the proposed CNN could separate water-fat images accurately. Although the proposed CNN was trained using only in vivo knee images, the proposed CNN could also ...Continue Reading

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Citations

Apr 30, 2019·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Jonathan AnderssonJoel Kullberg
Nov 25, 2020·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Kewen LiuChaoyang Liu
Feb 1, 2022·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Xinran ChenZhong Chen

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