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Compensation of Intra-Frame Head Motion in PET Data with Motion Corrected Independent Component Analysis (MCICA)
 

Summary: Compensation of Intra-Frame Head Motion in PET Data with Motion
Corrected Independent Component Analysis (MCICA)
Martin J. McKeown1,3,4
, Marwa Gadala2
, and Rafeef Abu-Gharbieh2,4
Departments of Medicine1
and Electrical and Computer Engineering2
, Pacific Parkinson's Research
Centre3
, Brain Research Centre4
, University of British Columbia
mmckeown@interchange.ubc.ca ,{mgadala, rafeef}@ece.ubc.ca
Keywords: Positron Emission Tomography, Motion Corrected Independent Component Analysis.
1. ABSTRACT
Independent Component Analysis (ICA) has proved a powerful exploratory analysis method for fMRI. In the ICA
model, the fMRI data at a given time point are modeled as the linear superposition of spatially independent (and
spatially stationary) component maps. The ICA model has been recently applied to positron emission tomography
(PET) data with some success (Human Brain Mapping 18:284-295(2003), IEEE Trans. BME, Naganawa et al, in press).
However, in PET imaging each frame is, in fact, activity integrated over a relatively long period of time, making the
assumption that the underlying component maps are spatially stationary (and hence no head movement has taken place

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences