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G08 A multicentre approach for the detection of patterns of impairment in Huntington's disease by using diffusion tensor imaging
  1. H-P Mueller1,
  2. R Sprengelmeyer1,
  3. SD Süssmuth1,
  4. G Groen2,
  5. NZ Hobbs3,
  6. RAC Roos4,
  7. A Dürr5,
  8. A Schoonderbeek4,
  9. E 't Hart4,
  10. R Valabrègue5,
  11. G B Landwehrmeyer1,
  12. J Kassubek1,
  13. SJ Tabrizi3
  1. 1Department of Neurology, University of Ulm, Ulm, Germany
  2. 2Department of Psychiatry, University of Ulm, Ulm, Germany
  3. 3Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
  4. 4Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands
  5. 5Neuroimaging Centre, CENIR, Paris, France


Aim Assessment of the feasibility of averaging diffusion tensor imaging (DTI) metrics of MRI data acquired in the course of a multicentre study.

Materials and methods 61 patients with early stage Huntington's disease and 40 healthy controls were studied at four study sites distributed across Europe using different MR scanner types with acquisition protocols that were as close as possible to a given standard protocol. Whole-brain based statistical comparison was performed on the basis of fractional anisotropy (FA) maps. The potential and feasibility of grouping and averaging data acquired at different sites was evaluated by comparison to previously published results. This approach seems to be suitable especially for DTI because of the measurement of physical quantities.

Results The affectation pattern of whole-brain based comparison of FA-maps showed reproducible results for all data sets of each study site as compared with previous results of other research groups. Averaging of all data sets of all study sites was performed and an improvement of the signal-to-noise ratio was found.

Conclusion Averaging of FA-maps of DTI data acquired from different subjects at multiple study sites with diverse MR scanner types has proven to be feasible. A suitable proposal is provided for testing the possibility of grouping multicentric DTI data to allow averaging of DTI-derived metrics to differentiate patients from healthy controls.

Funding This work has been supported by the European Union – PADDINGTON project, contract n. HEALTH-F2-2010-261358.

  • Diffusion tensor imaging
  • multicenter approach

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