Simultaneous BOLD detection and incomplete fMRI data reconstruction.
The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 4; pp. 599 - 611 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2018
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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=128549164&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128549164 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2018 vid: 56 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128549164 128549164 NLM28840461 10.1007/s11517-017-1707-x NLM28840461 128549164 ppf: 599 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Simultaneous BOLD detection and incomplete fMRI data reconstruction. aug: au: Ferdowsi, Saideh Abolghasemi, Vahid affil: Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran sug: subj: Image Processing, Computer Assisted Methods Magnetic Resonance Imaging Methods Oxygen Blood Brain Biochemical Phenomena Algorithms Ferrans and Powers Quality of Life Index Questionnaires ab: The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected. We propose a new cost function comprising both BOLD detection and data reconstruction terms. A solution based on singular value thresholding and sparsity-inducing approach is proposed. Due to the low-rank nature of the fMRI data, it is expected that the related techniques to be very effective for reconstruction. Extensive experiments are conducted on different datasets in noisy conditions. The achieved results, both in terms of data quality and data analysis accuracy, are promising and confirm that the proposed method can be effective for recovery of compressed/incomplete fMRI data. Several state-of-the art image reconstruction techniques are compared with the proposed method. In addition, the results of applying general linear model (GLM) using statistical parameter mapping (SPM) toolbox are compared with those of the proposed method. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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