Harmonization Benchmarking Tool for Neuroimaging Datasets

TL;DR

This paper introduces HABET, an open-source benchmarking tool designed to streamline the harmonization of neuroimaging datasets, enabling comparison of different algorithms to improve multi-site data analysis.

Contribution

The paper presents HABET, a novel tool that facilitates harmonization, comparison, and benchmarking of neuroimaging data processing techniques.

Findings

HABET successfully harmonized diffusion MRI images from ABCD study.

Comparison of two harmonization approaches demonstrated differences in performance.

HABET provides a standardized framework for evaluating harmonization algorithms.

Abstract

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.