Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors
This work addresses the need for harmonizing MRI brain volumes in multi-center studies, which is crucial for longitudinal assessment in clinical and research settings, though it appears incremental as it builds on existing harmonization methods by using image descriptors.
The paper tackled the problem of unreliable brain volume quantification in multi-center MRI studies due to hardware and software differences by proposing a novel approach using image descriptors with a Relevance Vector Machine (RVM) model. The method decreased scanner and center variability while preserving uncorrected measurements in multiple sclerosis patient data, improving reliability for longitudinal volumetric studies.
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.