Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration

Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

149 Citations (Scopus)

Abstract

This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.

Original languageEnglish
Pages (from-to)28-41
Number of pages14
JournalNeuroImage
Volume18
Issue number1
DOIs
Publication statusPublished - 2003

Bibliographical note

Funding Information:
We thank Dr. Susan Resnick and the BLSA for providing the data sets. This work was supported in part by NIH Grant R01 AG14971 and by NIH Contract AG-93-07.

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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