The optimal linear transformation-based fMRI feature space analysis.
This paper proposes a method of extending the optimal linear transformation (OLT), an image analysis technique of feature space, from magnetic resonance imaging (MRI) to functional magnetic resonance imaging (fMRI) so as to improve the activation detection performance over conventional approaches of...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 11; pp. 1119 - 1130 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Nov2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104907568&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104907568 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2009 vid: 47 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104907568 NLM19543931 2010453652 10.1007/s11517-009-0504-6 NLM19543931 104907568 ppf: 1119 ppct: 11 formats: fmt: @attributes: type: P tig: atl: The optimal linear transformation-based fMRI feature space analysis. aug: au: Sun F Morris D Babyn P Sun, Fengrong Morris, Drew Babyn, Paul affil: School of Information Science and Engineering, Shandong University, Jinan, Shandong, People's Republic of China sug: subj: Image Processing, Computer Assisted Magnetic Resonance Imaging Methods Radiographic Image Enhancement Human Validation Studies ab: This paper proposes a method of extending the optimal linear transformation (OLT), an image analysis technique of feature space, from magnetic resonance imaging (MRI) to functional magnetic resonance imaging (fMRI) so as to improve the activation detection performance over conventional approaches of fMRI analysis. The method was: (1) ideal hemodynamic responses for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing, (2) considering the ideal hemodynamic responses as hypothetical signature vectors for different activity patterns of interest, OLT was used to extract the 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, (3) 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 to validate the proposed method. The simulation and the experiment results indicated the proposed method was capable of improving some conventional methods to be more sensitive to activations, having strong contrast between activations and inactivations, and being more valid for complex activity patterns. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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