NDLI: A fast iterative reconstruction method based on the selective total variation for sparse angular CT

Content Provider	IEEE Xplore Digital Library
Author	Li, Huijun Zhang, Shuxu Yuan, Kehong Wang, Linjing Peng, Yingying
Copyright Year	2015
Description	Author affiliation: Biomedical Engineering Research Center, Tsinghua University, Shenzhen 518055, China (Yuan, Kehong) \|\| Radiotherapy Center, Cancer Center of Guangzhou Medical University, Affiliated Tumor Hospital of Guangzhou Medical University, Guangzhou 510095, China (Li, Huijun; Zhang, Shuxu; Wang, Linjing; Peng, Yingying)
Abstract	Sparse angular Computer Tomography (CT) is a rapidly developing imaging modality that reconstructs high-quality images from sparse data toward low-dose x-rays. The effectiveness of conventional total variation (TV) algorithm is limited by the over-smoothness on the edges and slow convergence. To mitigate this drawback, we proposed an improved fast iterative reconstruction method based on the minimization of selective image TV. The presented selective TV model is derived by linking the regularity metric to the local gradient of images, and selectively applies different degrees of regularization (the value of p) to background and potential signal locations for the purpose of preserving the edge details. In order to further speed up the convergence, we draws on a fast variant of The Iterative-Shrinkage-Thresholding Algorithm (ISTA), which uses a special linear combination of the two previous iterate results as the initial value of next iteration for more accurate correction. Experiments on simulated Shepp-Logan phantom are performed. The results demonstrated that the new method not only protected the edge of the image characteristics, but also significantly improved the convergence speed of the iterative reconstruction.
Starting Page	46
Ending Page	51
File Size	340480
Page Count	6
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	low dose radiation TV Image edge detection Computed tomography parse angular CT Imaging phantoms image reconstruction selective total variation Iterative methods fast iterative Image reconstruction Convergence
Content Type	Text
Resource Type	Article

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