Smoothed l0 Norm Regularization for Sparse-View X-Ray CT Reconstruction.
Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artif...
| Published in: | BioMed Research International Vol. 2016; pp. 1 - 13 |
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| Main Authors: | , , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
9/20/2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118218662&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118218662 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/20/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118218662 118218662 118218662 10.1155/2016/2180457 118218662 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Smoothed l0 Norm Regularization for Sparse-View X-Ray CT Reconstruction. aug: au: Li, Ming Zhang, Cheng Peng, Chengtao Guan, Yihui Xu, Pin Sun, Mingshan Zheng, Jian affil: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China sug: subj: Diagnostic Imaging Methods Tomography, X-Ray Computed Evaluation Radiography Radiation Adverse Effects Radiation Dosage Funding Source ab: Low-dose computed tomography (CT) reconstruction is a challenging problem in medical imaging. To complement the standard filtered back-projection (FBP) reconstruction, sparse regularization reconstruction gains more and more research attention, as it promises to reduce radiation dose, suppress artifacts, and improve noise properties. In this work, we present an iterative reconstruction approach using improved smoothed l0 (SL0) norm regularization which is used to approximate l0 norm by a family of continuous functions to fully exploit the sparseness of the image gradient. Due to the excellent sparse representation of the reconstruction signal, the desired tissue details are preserved in the resulting images. To evaluate the performance of the proposed SL0 regularization method, we reconstruct the simulated dataset acquired from the Shepp-Logan phantom and clinical head slice image. Additional experimental verification is also performed with two real datasets from scanned animal experiment. Compared to the referenced FBP reconstruction and the total variation (TV) regularization reconstruction, the results clearly reveal that the presented method has characteristic strengths. In particular, it improves reconstruction quality via reducing noise while preserving anatomical features. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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