A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization techniques, but there are few tools that streamline the process of harmonizing data, comparing across techniques, and benchmarking new techniques. In this paper, we introduce HArmonization BEnchmarking Tool (HABET), an open source tool for generating harmonized images and evaluating the performance of different harmonization algorithms. To demonstrate the capabilities of HABET, we harmonize diffusion MRI images from the Adolescent Brain and Cognitive Development (ABCD) study using two different approaches, and we compare their performance.
@article{arxiv.2211.07869,
title = {Harmonization Benchmarking Tool for Neuroimaging Datasets},
author = {Tom Osika and Ebrahim Ebrahim and Martin Styner and Marc Niethammer and Thomas Sawyer and Andinet Enquobahrie},
journal= {arXiv preprint arXiv:2211.07869},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication