ODVBA: Optimally-discriminative voxel-based analysis

Tianhao Zhang, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality.

Original languageEnglish (US)
Article number5712211
Pages (from-to)1441-1454
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume30
Issue number8
DOIs
Publication statusPublished - Aug 2011
Externally publishedYes

    Fingerprint

Keywords

  • Alzheimer's disease
  • Alzheimer's disease neuroimaging initiative (ADNI)
  • Gaussian smoothing
  • nonnegative discriminative projection (NDP)
  • optimally-discriminative voxel-based analysis
  • statistical parametric mapping
  • voxel-based morphometry

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

Cite this