Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI.
Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in AS...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 6; pp. 951 - 957 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2018
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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=129738985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129738985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2018 vid: 56 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129738985 129738985 NLM29105017 10.1007/s11517-017-1735-6 NLM29105017 129738985 ppf: 951 ppct: 6 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI. aug: au: Zhu, Hancan He, Guanghua Wang, Ze affil: School of Mathematics Physics and Information, Shaoxing University, 312000, Shaoxing, China sug: subj: Cerebrovascular Circulation Physiology Magnetic Resonance Imaging Methods Brain Image Processing, Computer Assisted Methods Brain Blood Supply Free Radicals ab: Arterial spin-labeling (ASL) perfusion MRI is a non-invasive method for quantifying cerebral blood flow (CBF). Standard ASL CBF calibration mainly relies on pair-wise subtraction of the spin-labeled images and controls images at each voxel separately, ignoring the abundant spatial correlations in ASL data. To address this issue, we previously proposed a multivariate support vector machine (SVM) learning-based algorithm for ASL CBF quantification (SVMASLQ). But the original SVMASLQ was designed to do CBF quantification for all image voxels simultaneously, which is not ideal for considering local signal and noise variations. To fix this problem, we here in this paper extended SVMASLQ into a patch-wise method by using a patch-wise classification kernel. At each voxel, an image patch centered at that voxel was extracted from both the control images and labeled images, which was then input into SVMASLQ to find the corresponding patch of the surrogate perfusion map using a non-linear SVM classifier. Those patches were eventually combined into the final perfusion map. Method evaluations were performed using ASL data from 30 young healthy subjects. The results showed that the patch-wise SVMASLQ increased perfusion map SNR by 6.6% compared to the non-patch-wise SVMASLQ. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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