Validating unbiased registration on longitudinal MRI scans from the Alzheimer's disease neuroimaging initiative (ADNI)

  • Yanovsky I
  • Thompson P
  • Osher S
 et al. 
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Abstract

This paper examines the power of different nonrigid registration models to detect changes in tensor based morphometry (TBM), and their stability when no real changes are present. Specifically, we investigate an asymmetric version of a recently proposed unbiased registration method, using mutual information as the matching criterion. We compare matching functionals (sum of squared differences and mutual information), as well as large deformation registration schemes (viscous fluid registration versus symmetric and asymmetric unbiased registration) for detecting changes in serial MRI scans of 10 elderly normal subjects and 10 patients with Alzheimer's disease scanned at 2-week and 1-year intervals. We demonstrated that the unbiased methods, both symmetric and asymmetric, have higher reproducibility. The unbiased methods were also less likely to detect changes in the absence of any real physiological change. Moreover, they measured biological deformations more accurately by penalizing bias in the corresponding statistical maps.

Author-supplied keywords

  • Image registration
  • Mutual information

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Authors

  • Igor Yanovsky

  • Paul M. Thompson

  • Stanley J. Osher

  • Xue Hua

  • David W. Shattuck

  • Arthur W. Toga

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