Document Detail


Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction.
MedLine Citation:
PMID:  23154621     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and other constraints, piecewise-smooth x-ray computed tomography (CT) can be reconstructed from sparse-view projection data without introducing notable artifacts. However, due to the piecewise constant assumption for the image, a conventional TV minimization algorithm often suffers from over-smoothness on the edges of the resulting image. To mitigate this drawback, we present an adaptive-weighted TV (AwTV) minimization algorithm in this paper. The presented AwTV model is derived by considering the anisotropic edge property among neighboring image voxels, where the associated weights are expressed as an exponential function and can be adaptively adjusted by the local image-intensity gradient for the purpose of preserving the edge details. Inspired by the previously reported TV-POCS (projection onto convex sets) implementation, a similar AwTV-POCS implementation was developed to minimize the AwTV subject to data and other constraints for the purpose of sparse-view low-dose CT image reconstruction. To evaluate the presented AwTV-POCS algorithm, both qualitative and quantitative studies were performed by computer simulations and phantom experiments. The results show that the presented AwTV-POCS algorithm can yield images with several notable gains, in terms of noise-resolution tradeoff plots and full-width at half-maximum values, as compared to the corresponding conventional TV-POCS algorithm.
Authors:
Yan Liu; Jianhua Ma; Yi Fan; Zhengrong Liang
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't     Date:  2012-11-15
Journal Detail:
Title:  Physics in medicine and biology     Volume:  57     ISSN:  1361-6560     ISO Abbreviation:  Phys Med Biol     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-11-20     Completed Date:  2013-05-03     Revised Date:  2013-07-11    
Medline Journal Info:
Nlm Unique ID:  0401220     Medline TA:  Phys Med Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  7923-56     Citation Subset:  IM    
Affiliation:
Department of Radiology, State University of New York, Stony Brook, NY 11794, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Image Processing, Computer-Assisted / methods*
Phantoms, Imaging
Radiation Dosage*
Tomography, X-Ray Computed / methods*
Grant Support
ID/Acronym/Agency:
CA082402/CA/NCI NIH HHS; CA143111/CA/NCI NIH HHS; R01 CA082402/CA/NCI NIH HHS; R01 CA143111/CA/NCI NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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