Abstract
We present a new method for the analysis of individual brain positron emission tomography (PET) activation maps that looks for activated areas of a certain size rather than pixels with maximum values. High signal-to-noise-ratio pixel clusters (HSC) are identified and their sizes are statistically tested with respect to a Monte-Carlo–derived distribution of cluster sizes in pure noise images. From multiple HSC size tests, a strategy is proposed for control of the overall type I error. The sensitivity and specificity of this method have been assessed using realistic Monte Carlo simulations of brain activation maps. When compared with the γ2 statistic of the local maxima distribution, the proposed method showed enhanced sensitivity, particularly for signals of low magnitude and/or large size. Its potential for the individual analysis of PET activation studies is presented in two sets of subjects who underwent two cognitive protocols. Although it can be viewed as an alternative to the classical stereotactic averaging approach, this new method is intended to be a first step toward the analysis of single-subject PET activation studies.
Keywords
Positron emission tomographyMonte Carlo methodPixelSensitivity (control systems)Noise (video)StatisticPhysicsSIGNAL (programming language)Signal-to-noise ratio (imaging)PositronStatistical noiseAlgorithmComputer scienceArtificial intelligencePattern recognition (psychology)Statistical physicsMathematicsNuclear medicineStatisticsOpticsMedicineElectronImage (mathematics)Nuclear physicsElectronic engineering
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Publication Info
- Year
- 1993
- Type
- article
- Volume
- 13
- Issue
- 3
- Pages
- 425-437
- Citations
- 157
- Access
- Closed
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Cite This
Jean‐Baptiste Poline,
Bernard Mazoyer
(1993).
Analysis of Individual Positron Emission Tomography Activation Maps by Detection of High Signal-to-Noise-Ratio Pixel Clusters.
Journal of Cerebral Blood Flow & Metabolism
, 13
(3)
, 425-437.
https://doi.org/10.1038/jcbfm.1993.57
Identifiers
- DOI
- 10.1038/jcbfm.1993.57