Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors

Maria Ines Meyer*, Ezequiel Rosa, Koen Leemput, Diana Sima

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorithms for brain volume quantification are unreliable for the longitudinal assessment of volume changes in this type of setting. Currently most methods attempt to decrease this issue by regressing the scanner- and/or center-effects from the original data. In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors. First, we explore the relationships between volumes and image descriptors. Then, we train a Relevance Vector Machine (RVM) model over a large multi-site dataset of healthy subjects to perform volume harmonization. Finally, we validate the method over two different datasets: (i) a subset of unseen healthy controls; and (ii) a test-retest dataset of multiple sclerosis (MS) patients. The method decreases scanner and center variability while preserving measurements that did not require correction in MS patient data. We show that image descriptors can be used as input to a machine learning algorithm to improve the reliability of longitudinal volumetric studies.
Original languageEnglish
Title of host publicationOR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging : Proceedings of Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019
PublisherSpringer
Publication date2019
Pages77-85
DOIs
Publication statusPublished - 2019
SeriesLecture Notes in Computer Science
Volume11796
ISSN0302-9743

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