Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors

TL;DR

This paper introduces a novel MRI volume harmonization method using relevance vector machines and image descriptors, reducing scanner variability and improving longitudinal brain volume assessments across multi-center studies.

Contribution

The study presents a new approach employing RVMs with image descriptors for MRI harmonization, outperforming traditional methods that regress scanner effects.

Findings

Reduces scanner and center variability in MRI measurements.

Preserves accurate volume measurements in MS patient data.

Improves reliability of longitudinal brain volume studies.

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.