Harmonization of Brain Diffusion MRI: Concepts and Methods

Abstract

MRI diffusion data suffers from significant inter-and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusionweighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan

“…Finally, as for diffusion MRI, the specialized nature of dMRI signals and associated analysis methods has led to dMRI-specific harmonization methods, as recently proposed for harmonizing the multi-site & multi-shell data based on model-free approaches (see Pinto et al, 2020 for review). Ning et al (2020) compared the effects of several dMRI harmonization methods using the multi-shell diffusion MRI data in the same subjects in multiple scanners.…”

Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

“…Harmonization methods [20] have been proposed to remove those undesirable scanner effects or solve the incomparability among MRI images, thus improving the reproducibility of multicenter radiomic studies on MRI datasets. We can classify the harmonization methods for MRI radiomic studies into two classes, namely (i) harmonizing the MRI images before the feature extraction and (ii) harmonizing the extracted radiomic features.…”

Harmonization of diffusionMRIdata sets with adaptive dictionary learning

“…Even by carefully controlling all these sources of variability as much as possible, there still remain reproducibility issues between scanners of the same model or in scan‐rescan studies of dMRI metrics (Kristo et al, 2013; Magnotta et al, 2012; Vollmar et al, 2010). Over the years, many algorithms have been developed to mitigate the variability attributed to nonbiological effects in dMRI, for example, in order to combine data sets from multiple studies and increase statistical power, see for example, (Pinto et al, 2020; Tax et al, 2019; Zhu, Moyer, Nir, Thompson, & Jahanshad, 2019) for reviews. Common approaches consist in harmonizing the dMRI data sets through the coefficients of a spherical harmonics representation (Blumberg et al, 2019; Cetin Karayumak et al, 2019; Mirzaalian et al, 2016) or the computed scalar metrics (Fortin et al, 2017; Pohl et al, 2016) to reduce variability between scanners.…”