Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

NeuroImage

Blake E. Dewey¹  Yihao Liu²  Lianrui Zuo³  Ellen M. Mowry⁴  Scott D. Newsome⁴  Yufan He⁴  Jerry L. Prince⁵  Susan M. Resnick⁵  Aaron Carass⁶ 

[1] Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA;Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA;Corresponding author.;Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218 USA;Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA;Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institute of Health, Baltimore, MD 20892, USA;

关键词: Harmonization;    Magnetic resonance imaging;    Disentangle;    Image synthesis;    Image-to-image translation;   

DOI  :  

来源: DOAJ

【 摘 要 】

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, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

【 授权许可】

Unknown