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Activation detection in fMRI data via multi-scale singularity detection

Ralf Mekle; Andrew F. Laine; Gerard M. Perera; Robert L. Delapaz

Title:
Activation detection in fMRI data via multi-scale singularity detection
Author(s):
Mekle, Ralf
Laine, Andrew F.
Perera, Gerard M.
Delapaz, Robert L.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Book/Journal Title:
Wavelet applications in signal and image processing VIII : 31 July-4 August 2000, San Diego, USA ; Proceedings of SPIE, vol. 4119
Book Author:
Aldroubi, Akram
Publisher:
SPIE
Publisher Location:
Bellingham, Wash.
Abstract:
Detection of active areas in the brain by functional magnetic resonance imaging (fMRI) is a challenging problem in medical imaging. Moreover, determining the onset and end of activation signal at specific locations in 3-space can determined networks of temporal relationships required for brain mapping. We introduce a method for activation detection in fMRI data via wavelet analysis of singular features. We pose the problem of determining activated areas as singularity detection in the temporal domain. Overcomplete wavelet expansion at integer scales are used to trace wavelet modulus maxima across scales to construct maxima lines. Form these maxima lines, singularities in the signal are located corresponding to the onset and end of an activation signal. We present result for simulated phantom waveforms and clinical fMRI dat from human finger tapping experiments. Different levels of noise were added to two waveforms of phantom data. No assumptions about specific frequency and amplitude of an activation signal were made prior to analysis. Detection was reliable for modest levels of random noise, but less precise at higher levels. For clinical fMRI data, activation maps were comparable to those of existing standard techniques.
Subject(s):
Biomedical engineering
Publisher DOI:
10.1117/12.408651
Item views:
139
Metadata:
text | xml

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