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.
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Notes
In this work, we computed the RISH features for order {0,2,4,6,8} and ignored the higher order terms as they are very high frequency terms primarily capturing noise in the data.
References
Avants, B., Tustison, N., & Johnson, H. (2014). Advanced Normalization Tools.
Cannon, T., McEwen, F.S.S., abd G., He, X.P., Erp, T., Jacobson, A., Beardon, C., & Walker, E. (2014). Reliability of neuroanatomical measurements in a multi-site longitudinal study of youth at risk for psychosis. Human Brain Mapping, 35, 2424–2434. In press.
Dariya, I., & et al. (2016). Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials. Magnetic Resonance in Medicine, 75, 1312–1323.
Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007). Regularized, fast, and robust analytical q-ball imaging. MRM, 58, 497–510.
Forsyth, J., & Cannon, T. (2014). Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the north american prodrome longitudinal study. Neuroimage, 97, 41–52.
Giannelli, M., Sghedoni, R., Iacconi, C., Iori, M., Traino, A., Guerrisi, M., Mascalchi, M., Toschi, N., & Diciotti, S. (2014). MR scanner systems should be adequately characterized in diffusion-MRI of the breast. PLoS One, 9, 862–880.
Jahanshad, N., & et al. (2013). Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the enigma–dti working group. NeuroImage, 455–469.
Kochunov, P., & et al. (2014). Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and mega analytical approaches for data pooling. NeuroImage, 95, 136–150.
Magnotta, V., Matsui, J., Liu, D., Johnson, H., Long, J., Bolster, B., Mueller, J., Lim, K., Mori, S., Helmer, K., Turner, J., Reading, S., Lowe, M., Aylward, E., Flashman, L., Bonett, G., & Paulsen, J. (2012). Multicenter reliability of diffusion tensor imaging. Brain Connectivity, 2, 345–355.
Matsui, J. (2014). Development of image processing tools and procedures for analyzing multi-site longitudinal diffusion-weighted imaging studies. Phd Thesis, University of IowaFollow.
Mirzaalian, H., Ning, L., Savadjiev, P., Pasternak, O., Bouix, S., Michailovich, O., Grant, G., Marx, C.E., Morey, R.A., Flashman, L.A., George, M.S., McAllister, T., Andaluz, N., Shutter, L., Coimbra, R., Zafonte11, R.D., Coleman, M.J., Kubicki1, M., Westin, C.F., Stein1, M.B., Shenton, M.E., & Rathi, Y. (2016). Inter-site and inter-scanner diffusion mri data harmonization. NeuroImage, 311–323.
Mirzaalian, H., Pierrefeu, A., Savadjiev, P., Pasternak, O., Bouix, S., Kubicki, M., Westin, C. F., Shenton, M.E., & Rathi, Y. (2015). Harmonizing diffusion mri data across multiple sites and scanners, (pp. 12–19): MICCAI.
Özarslan, E., Shepherd, T.M., Vemuri, B.C., Blackband, S.J., & Mareci, T.H. (2006). Resolution of complex tissue microarchitecture using the diffusion orientation transform (dot). NeuroImage, 31, 1086–1103.
Pohl, K., & et al. (2016). Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the ncanda study. NeuroImage, 130, 194– 213.
Salimi-Khorshidi, G., Smith, S., Keltner, J., Wager, T., & Nichols, T. (2009). Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage, 25, 810–823.
Smitha, S., Jenkinsona, M., Johansen-Berga, H., Rueckertb, D., Nicholsc, T., Mackaya, C., Watkinsa, K., Ciccarellid, O., Cadera, Z., Matthewsa, P., & Behrensa, T. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31, 1487–1505.
Tournier, J.D., Calamante, F., & Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 35, 1459–1472.
Venkatraman, V., Gonzalez, C., Landman, B., Goh, J., Reiter, D., An, Y., & Resnick, S. (2015). Region of interest correction factors improve reliability of diffusion imaging measures within and across scanners and field strengths. NeuroImage, 16–25.
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The authors would like to acknowledge the following grants which supported this work: W81XWH-08-2- 0159 (Imaging Core PI: Shenton, Contact PI: Stein, Site PIs: George, Grant, Marx, McCallister, Zafonte; Other: Bouix, Coleman, Bouix, Kubicki, Mirzaalian, Pasternak, Savadjiev, Rathi), R01MH099797 (PI: Rathi), R01MH074794 (PI: Westin), P41EB015902 (PI: Kikinis), Swedish Research Council (VR) grant 2012-3682, Swedish Foundation for Strategic Research (SSF) grant AM13-0090, and VA Merit (PI: Shenton).
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed written consent was obtained from human participants, who were recruited based on approval from local Institutional review board (IRBs).
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Mirzaalian, H., Ning, L., Savadjiev, P. et al. Multi-site harmonization of diffusion MRI data in a registration framework. Brain Imaging and Behavior 12, 284–295 (2018). https://doi.org/10.1007/s11682-016-9670-y
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DOI: https://doi.org/10.1007/s11682-016-9670-y