Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory
Abstract
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...
Paper Details
Title
Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory
Published Date
Nov 1, 2021
Journal
Volume
243
Pages
118569 - 118569
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