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Fat-saturated image generation from multi-contrast MRIs using generative adversarial networks with Bloch equation-based autoencoder regularization

Authors
 Sewon Kim  ;  Hanbyol Jang  ;  Seokjun Hong  ;  Yeong Sang Hong  ;  Won C Bae  ;  Sungjun Kim  ;  Dosik Hwang 
Citation
 MEDICAL IMAGE ANALYSIS, Vol.73 : 102198, 2021-10 
Journal Title
MEDICAL IMAGE ANALYSIS
ISSN
 1361-8415 
Issue Date
2021-10
Keywords
Autoencoder regularization ; Bloch equation ; Geneartive adversarial networks ; Image synthesis ; Magnetic resonance image ; Multi-contrast imaging
Abstract
Obtaining multiple series of magnetic resonance (MR) images with different contrasts is useful for accurate diagnosis of human spinal conditions. However, this can be time consuming and a burden on both the patient and the hospital. We propose a Bloch equation-based autoencoder regularization generative adversarial network (BlochGAN) to generate a fat saturation T2-weighted (T2 FS) image from T1-weighted (T1-w) and T2-weighted (T2-w) images of human spine. To achieve this, our approach was to utilize the relationship between the contrasts using Bloch equation since it is a fundamental principle of MR physics and serves as a physical basis of each contrasts. BlochGAN properly generated the target-contrast images using the autoencoder regularization based on the Bloch equation to identify the physical basis of the contrasts. BlochGAN consists of four sub-networks: an encoder, a decoder, a generator, and a discriminator. The encoder extracts features from the multi-contrast input images, and the generator creates target T2 FS images using the features extracted from the encoder. The discriminator assists network learning by providing adversarial loss, and the decoder reconstructs the input multi-contrast images and regularizes the learning process by providing reconstruction loss. The discriminator and the decoder are only used in the training process. Our results demonstrate that BlochGAN achieved quantitatively and qualitatively superior performance compared to conventional medical image synthesis methods in generating spine T2 FS images from T1-w, and T2-w images.
Full Text
https://www.sciencedirect.com/science/article/pii/S1361841521002437
DOI
10.1016/j.media.2021.102198
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungjun(김성준) ORCID logo https://orcid.org/0000-0002-7876-7901
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/185372
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