Multi-site harmonization of diffusion MRI data in a registration framework.
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 neuroi...
| Publicado en: | Brain Imaging & Behavior Vol. 12; no. 1; pp. 284 - 296 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128033925&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128033925 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Feb2018 vid: 12 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128033925 128033925 NLM28176263 128033925 10.1007/s11682-016-9670-y NLM28176263 128033925 ppf: 284 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-site harmonization of diffusion MRI data in a registration framework. aug: au: Mirzaalian, Hengameh Ning, Lipeng Savadjiev, Peter Pasternak, Ofer Bouix, Sylvain Michailovich, Oleg Karmacharya, Sarina Grant, Gerald Marx, Christine E. Morey, Rajendra A. Flashman, Laura A. George, Mark S. McAllister, Thomas W. Andaluz, Norberto Shutter, Lori Coimbra, Raul Zafonte, Ross D. Coleman, Mike J. Kubicki, Marek Westin, Carl-Fredrik affil: Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA sug: subj: Image Processing, Computer Assisted Methods Magnetic Resonance Imaging Methods Brain Brain Pathology Male Female Models, Biological Human Magnetic Resonance Imaging Equipment and Supplies Computer Simulation Adult Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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