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 language | English (US) |
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Article number | 6310063 |
Pages (from-to) | 223-236 |
Number of pages | 14 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 32 |
Issue number | 2 |
DOIs | |
State | Published - 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