Advertisement

Brain Imaging and Behavior

, Volume 12, Issue 1, pp 284–295 | Cite as

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

  • Hengameh Mirzaalian
  • Lipeng Ning
  • Peter Savadjiev
  • Ofer Pasternak
  • Sylvain Bouix
  • Oleg Michailovich
  • Sarina Karmacharya
  • Gerald Grant
  • Christine E. Marx
  • Rajendra A. Morey
  • Laura A. Flashman
  • Mark S. George
  • Thomas W. McAllister
  • Norberto Andaluz
  • Lori Shutter
  • Raul Coimbra
  • Ross D. Zafonte
  • Mike J. Coleman
  • Marek Kubicki
  • Carl-Fredrik Westin
  • Murray B. Stein
  • Martha E. Shenton
  • Yogesh Rathi
Brief Communication

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.

Keywords

Diffusion MRI Harmonization Multi-site Inter-scanner Intra-site 

Notes

Compliance with Ethical Standards

Conflict of interests

no conflict.

Funding

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).

Ethical approval

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).

References

  1. Avants, B., Tustison, N., & Johnson, H. (2014). Advanced Normalization Tools.Google Scholar
  2. 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.CrossRefPubMedGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007). Regularized, fast, and robust analytical q-ball imaging. MRM, 58, 497–510.CrossRefPubMedGoogle Scholar
  5. 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.CrossRefPubMedGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.Google Scholar
  8. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Matsui, J. (2014). Development of image processing tools and procedures for analyzing multi-site longitudinal diffusion-weighted imaging studies. Phd Thesis, University of IowaFollow.Google Scholar
  11. 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.Google Scholar
  12. 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.Google Scholar
  13. 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.CrossRefPubMedGoogle Scholar
  14. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefPubMedGoogle Scholar
  18. 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.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Hengameh Mirzaalian
    • 1
    • 14
  • Lipeng Ning
    • 1
  • Peter Savadjiev
    • 1
  • Ofer Pasternak
    • 1
  • Sylvain Bouix
    • 1
  • Oleg Michailovich
    • 2
  • Sarina Karmacharya
    • 1
  • Gerald Grant
    • 3
  • Christine E. Marx
    • 4
  • Rajendra A. Morey
    • 4
  • Laura A. Flashman
    • 5
  • Mark S. George
    • 6
  • Thomas W. McAllister
    • 7
  • Norberto Andaluz
    • 8
  • Lori Shutter
    • 9
  • Raul Coimbra
    • 10
  • Ross D. Zafonte
    • 11
  • Mike J. Coleman
    • 1
  • Marek Kubicki
    • 1
  • Carl-Fredrik Westin
    • 1
  • Murray B. Stein
    • 12
  • Martha E. Shenton
    • 1
    • 13
  • Yogesh Rathi
    • 1
  1. 1.Harvard Medical School and Brigham and Women’s HospitalBostonUSA
  2. 2.University of WaterlooWaterlooCanada
  3. 3.Stanford University Medical Center (Previously Duke University)Palo AltoUSA
  4. 4.Medical Center and VA Mid-Atlantic MIRECCDuke UniversityDurhamUSA
  5. 5.Hanover and Geisel School of Medicine at DartmouthDartmouth UniversityHanoverUSA
  6. 6.Ralph H. Johnson VA Medical CenterMedical University of South CarolinaCharlestonUSA
  7. 7.Geisel School of Medicine at Dartmouth (original) and Indiana University School of Medicine (current)HanoverUSA
  8. 8.Department of NeurosurgeryUniversity of Cincinnati (UC) College of Medicine; Neurotrauma Center at UC Neuroscience Institute; and Mayfield ClinicCincinnatiUSA
  9. 9.University of Pittsburgh School of Medicine (Previously University of Cincinnati)PittsburghUSA
  10. 10.Department of SurgeryUniversity of CaliforniaSan DiegoUSA
  11. 11.Spaulding Rehabilitation Hospital and Harvard Medical SchoolBostonUSA
  12. 12.University of California, San DiegoSan DiegoUSA
  13. 13.VA Boston Healthcare SystemBostonUSA
  14. 14.Harvard Medical School and Boston Children’s HospitalBostonUSA

Personalised recommendations