Multi-site harmonization of diffusion MRI data in a registration framework

Abstract

Diffusion MRI (dMRI) data acquired on different scanners varies significantly in its content throughout the brain even if the acquisition parameters are nearly identical. Thus, proper harmonization of such data sets is necessary to increase the sample size and thereby the statistical power of neuroimaging studies. In this paper, we present a novel approach to harmonize dMRI data (the raw signal, instead of dMRI derived measures such as fractional anisotropy) using rotation invariant spherical harmonic (RISH) features embedded within a multi-modal image registration framework. All dMRI data sets from all sites are registered to a common template and voxel-wise differences in RISH features between sites at a group level are used to harmonize the signal in a subject-specific manner. We validate our method on diffusion data acquired from seven different sites (two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across these sites before and after data harmonization. Validation was also done on a group oftest subjects, which were not used to "learn" the harmonization parameters. We also show results using TBSS before and after harmonization for independent validation of the proposed methodology. Using synthetic data, we show that any abnormality in diffusion measures due to disease is preserved during the harmonization process. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences in the signal can be removed using the proposed method in a model independent manner.

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

“…For example, Kochunov et al (2018) calculated the signal to noise ratio for each protocol and include it in their regression models. Mirzaalian et al (2018) use voxel-wise spherical harmonic residual networks to derive local correction parameters. Finding the best method to harmonize dMRI data is an active topic at 'hackathons' and technical challenges; in 2017 and 2018, the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) hosted a computational diffusion MRI challenge to explore approaches for data harmonization.…”

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers—An ex vivo validation study in the squirrel monkey brain

“…We note that here we assume a single q ‐space shell, or single b ‐value, acquisition (as the signal and response are also dependent on acquisition parameters including b value). Inverting this gives where r l is only defined for m = 0 (for axial symmetry of the response function around the z axis), and p l and s l are vectors of coefficients (one element for each m value), for each order. Alternately, by multiplying both sides of Equation by p lm and summing over m , the response function can be estimated from …”

“…where r l is only defined for m = 0 (for axial symmetry of the response function around the z axis 27,32,45 ), and p l and s l are vectors of coefficients (one element for each m value), for each order. Alternately, by multiplying both sides of Equation 2 by p lm and summing over m, the response function can be estimated from…”

Fast qualitYconTrol meThod foRderIved diffUsionMetrics (YTTRIUM) in big data analysis: UK Biobank 18608 example

“…However, a quality evaluation of the processed diffusion maps is still an open question in big data analysis. Many efforts have been made to develop accurate QC and harmonisation procedures on raw diffusion weighted data (Mirzaalian et al 2018), (Fortin et al 2017). Nevertheless, derived diffusion metrics from DTI or DKI may still deviate from expected range, e.g.…”

“…However, QC and data harmonisation procedures applied on raw diffusion data (Mirzaalian et al 2018), (Fortin et al 2017) do not guarantee accurate numerical computation of scalar diffusion metrics. Derived diffusion metrics from diffusion or kurtosis tensors are sensitive to a range of subjectspecific factors such as age or various brain disorders, but also to applied numerical algorithm or its programming implementation (Lebel et al 2012), (Grinberg et al 2017), (Maximov et al 2015), (David et al 2019).…”