Combining boundary-based methods with tensor-based morphometry in the measurement of longitudinal brain change

Evan Fletcher, Alexander Knaack, Baljeet Singh, Evan Lloyd, Evan Wu, Owen Carmichael, Charles DeCarli

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.

Original languageEnglish (US)
Article number6310063
Pages (from-to)223-236
Number of pages14
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number2
DOIs
StatePublished - 2013

Keywords

  • Biomedical imaging
  • brain boundary shift
  • image matching
  • image registration
  • Kullback-Liebler
  • tensor-based morphometry (TBM)

ASJC Scopus subject areas

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

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