  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.
