Name
Abstract | ||
|---|---|---|
The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TITB.2010.2055574 | IEEE Transactions on Information Technology in Biomedicine |
Keywords | Field | DocType |
applied signature vector,fmri data,olt,general linear model comparison,hemodynamic response time series,stimulus timing,image analysis technique,brain activity,hemodynamic response model,linear transformation,feature space based fmri analysis,fmri analysis,fmri composite image,biomedical mri,fmri,activation-detection performance,fmri experiment,hypothetical signature vectors,functional mri (fmri) analyses,functional mri,transforms,feature space,activity pattern,glm-based analysis,feature-space-based fmri analysis,medical image processing,optimal linear transformation,haemodynamics,signal to noise ratio,hemodynamics,image analysis,feature extraction,time series analysis,magnetic resonance imaging,hemodynamic response,general linear model,time series | Time series,Computer vision,Feature vector,General linear model,Linear model,Computer science,Signal-to-noise ratio,Feature extraction,Linear map,Artificial intelligence,Cluster analysis | Journal |
Volume | Issue | ISSN |
14 | 5 | 1558-0032 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng-rong Sun | 1 | 13 | 3.91 |
| Drew Morris | 2 | 9 | 1.27 |
| Wayne Lee | 3 | 11 | 0.88 |
| Margot J Taylor | 4 | 110 | 27.22 |
| Travis Mills | 5 | 9 | 1.06 |
| Paul Babyn | 6 | 166 | 21.42 |