A Disentangled Latent Space for Cross-Site MRI Harmonization

Blake E. Dewey, Lianrui Zuo, Aaron Carass, Yufan He, Yihao Liu, Ellen M. Mowry, Scott Newsome, Jiwon Oh, Peter A. Calabresi, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate interpretation and quantification of magnetic resonance imaging (MRI) is vital to medical research and clinical practice. However, lack of MRI standardization and differences in acquisition protocols often lead to measurement inconsistencies across sites. Image harmonization techniques have been shown to improve qualitative and quantitative consistency between differently acquired scans. Unfortunately, these methods typically require paired training data from traveling subjects (for supervised methods) or assumptions about anatomical similarities between the populations (for unsupervised methods). We propose a deep learning-based harmonization technique with limited supervision for use in standardization across scanners and sites. By leveraging a disentangled latent space represented by a high-resolution anatomical information component (β) and a low-dimensional contrast component (θ), the proposed method trains a cross-site harmonization model using databases of multi-modal image pairs acquired separately from each of the scanners to be harmonized. In this manuscript, we show that by using T1-weighted and T2-weighted images acquired from different subjects at three different sites, we can achieve a stable extraction of β with a continuous representation of θ. We also demonstrate that this allows cross-site harmonization without the need for paired data between sites.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages720-729
Number of pages10
ISBN (Print)9783030597276
DOIs
StatePublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/8/20

Keywords

  • Deep learning
  • MR harmonization
  • Multiple sclerosis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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