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

Brain Imaging and Behavior volume 12, pages 284–295 (2018)Cite this article

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

  1. 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.

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Affiliations

  1. Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Hengameh Mirzaalian, Lipeng Ning, Peter Savadjiev, Ofer Pasternak, Sylvain Bouix, Sarina Karmacharya, Mike J. Coleman, Marek Kubicki, Carl-Fredrik Westin, Martha E. Shenton & Yogesh Rathi

  2. University of Waterloo, Waterloo, ON, Canada

    Oleg Michailovich

  3. Stanford University Medical Center (Previously Duke University), Palo Alto, CA, USA

    Gerald Grant

  4. Medical Center and VA Mid-Atlantic MIRECC, Duke University, Durham, NC, USA

    Christine E. Marx & Rajendra A. Morey

  5. Hanover and Geisel School of Medicine at Dartmouth, Dartmouth University, Hanover, NH, USA

    Laura A. Flashman

  6. Ralph H. Johnson VA Medical Center, Medical University of South Carolina, Charleston, SC, USA

    Mark S. George

  7. Geisel School of Medicine at Dartmouth (original) and Indiana University School of Medicine (current), 1 Rope Ferry Rd, Hanover, NH, 03755, USA

    Thomas W. McAllister

  8. Department of Neurosurgery, University of Cincinnati (UC) College of Medicine; Neurotrauma Center at UC Neuroscience Institute; and Mayfield Clinic, Cincinnati, OH, USA

    Norberto Andaluz

  9. University of Pittsburgh School of Medicine (Previously University of Cincinnati), Pittsburgh, PA, USA

    Lori Shutter

  10. Department of Surgery, University of California, San Diego, CA, USA

    Raul Coimbra

  11. Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, MA, USA

    Ross D. Zafonte

  12. University of California, San Diego, San Diego, CA, USA

    Murray B. Stein

  13. VA Boston Healthcare System, Boston, MA, USA

    Martha E. Shenton

  14. Harvard Medical School and Boston Children’s Hospital, Boston, MA, USA

    Hengameh Mirzaalian

Authors
  1. Hengameh Mirzaalian
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  2. Lipeng Ning
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  3. Peter Savadjiev
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  4. Ofer Pasternak
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  5. Sylvain Bouix
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  6. Oleg Michailovich
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  7. Sarina Karmacharya
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  8. Gerald Grant
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  9. Christine E. Marx
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  10. Rajendra A. Morey
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  11. Laura A. Flashman
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  12. Mark S. George
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  13. Thomas W. McAllister
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  14. Norberto Andaluz
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  15. Lori Shutter
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  16. Raul Coimbra
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  17. Ross D. Zafonte
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  18. Mike J. Coleman
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  19. Marek Kubicki
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  20. Carl-Fredrik Westin
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  21. Murray B. Stein
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  22. Martha E. Shenton
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  23. Yogesh Rathi
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Corresponding author

Correspondence to Hengameh Mirzaalian.

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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).

Appendix

Appendix

Table 7 P-values before and after harmonization for MD, FA, GFA for different sites and ROIs applying the harmonization method at Mirzaalian et al. (2015)
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Table 8 P-values before and after harmonization for MD, FA, GFA for different sites and ROIs using test data excluded from training applying the harmonization method at Mirzaalian et al. (2015)
Full size table

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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

Keywords

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