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Neuroimage. 2016 Jul 15;135:311-23. doi: 10.1016/j.neuroimage.2016.04.041. Epub 2016 Apr 30.

Inter-site and inter-scanner diffusion MRI data harmonization.

Author information

Harvard Medical School and Brigham and Women's Hospital, Boston, USA. Electronic address:
Harvard Medical School and Brigham and Women's Hospital, Boston, USA.
University of Waterloo, Canada.
Stanford University Medical Center, Palo Alto, CA, USA (Previously Duke University).
Duke University Medical Center and VA Mid-Atlantic MIRECC, NC, USA.
Dartmouth University, Hanover and Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Medical University of South Carolina, Charleston, SC, USA, Ralph H. Johnson VA Medical Center, Charleston.
Geisel School of Medicine at Dartmouth (original) and Indiana University School of Medicine (current).
Department of Neurosurgery, University of Cincinnati (UC) College of Medicine; Neurotrauma Center at UC Neuroscience Institute; and Mayfield Clinic, Cincinnati, OH.
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA (Previously Duke University).
Department of Surgery, University of California, San Diego.
Spaulding Rehabilitation Hospital and Harvard Medical School, Boston, USA.
University of California, San Diego, San Diego, CA, USA.
Harvard Medical School and Brigham and Women's Hospital, Boston, USA; VA Boston Healthcare System, Boston, MA, USA.


We propose a novel method to harmonize diffusion MRI data acquired from multiple sites and scanners, which is imperative for joint analysis of the data to significantly increase sample size and statistical power of neuroimaging studies. Our method incorporates the following main novelties: i) we take into account the scanner-dependent spatial variability of the diffusion signal in different parts of the brain; ii) our method is independent of compartmental modeling of diffusion (e.g., tensor, and intra/extra cellular compartments) and the acquired signal itself is corrected for scanner related differences; and iii) inter-subject variability as measured by the coefficient of variation is maintained at each site. We represent the signal in a basis of spherical harmonics and compute several rotation invariant spherical harmonic features to estimate a region and tissue specific linear mapping between the signal from different sites (and scanners). We validate our method on diffusion data acquired from seven different sites (including two GE, three Philips, and two Siemens scanners) on a group of age-matched healthy subjects. Since the extracted rotation invariant spherical harmonic features depend on the accuracy of the brain parcellation provided by Freesurfer, we propose a feature based refinement of the original parcellation such that it better characterizes the anatomy and provides robust linear mappings to harmonize the dMRI data. We demonstrate the efficacy of our method by statistically comparing diffusion measures such as fractional anisotropy, mean diffusivity and generalized fractional anisotropy across multiple sites before and after data harmonization. We also show results using tract-based spatial statistics before and after harmonization for independent validation of the proposed methodology. Our experimental results demonstrate that, for nearly identical acquisition protocol across sites, scanner-specific differences can be accurately removed using the proposed method.


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

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