A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression.
Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal bra...
| Publicado en: | BioMed Research International pp. 1 - 16 |
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| Autores principales: | , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/8/2021
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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=149334811&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149334811 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/8/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149334811 149334811 149334811 10.1155/2021/6685943 149334811 ppf: 1 ppct: 15 formats: fmt: @attributes: type: P tig: atl: A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression. aug: au: Ni, Qian Zhang, Yi Wen, Tiexiang Li, Ling affil: Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China sug: subj: Brain Embryology Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Human Imaging, Three-Dimensional Algorithms Artifacts Fetus Fetus, conception to birth ab: Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal brain MRI from multiple stacks corrupted with motion artifacts. Based on the SVR framework, our approach includes the slice-to-volume 2D/3D registration, the point spread function- (PSF-) based volume update, and the adaptive kernel regression-based volume update. The adaptive kernel regression can deal well with the sparse sampling data and enhance the detailed preservation by capturing the local structure through covariance matrix. Experimental results performed on clinical data show that kernel regression results in statistical improvement of image quality for sparse sampling data with the parameter setting of the structure sensitivity 0.4, the steering kernel size of 7 × 7 × 7 and steering smoothing bandwidth of 0.5. The computational performance of the proposed GPU-based method can be over 90 times faster than that on CPU. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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