Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, C...

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Bibliographic Details
Main Authors: Lianrui Zuo, Blake E. Dewey, Yihao Liu, Yufan He, Scott D. Newsome, Ellen M. Mowry, Susan M. Resnick, Jerry L. Prince, Aaron Carass
Format: Article
Language:English
Published: Elsevier 2021-11-01
Series:NeuroImage
Subjects:
Harmonization
Magnetic resonance imaging
Disentangle
Image synthesis
Image-to-image translation
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811921008429

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http://www.sciencedirect.com/science/article/pii/S1053811921008429

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