GPU-Accelerated Dynamic Wavelet Thresholding Algorithm for X-Ray CT Metal Artifact Reduction

The computed tomography (CT) imaging technique has been used to diagnose widespread disease for many years; however, severe streaking artifacts have been observed to appear in the reconstructed images of examinate contains metal objects. In this paper, we propose a dynamic wavelet thresholding metal...

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Vydané v:IEEE transactions on radiation and plasma medical sciences Ročník 2; číslo 1; s. 17 - 26
Hlavní autori: Peng, Chengtao, Qiu, Bensheng, Li, Ming, Yang, Yang, Zhang, Cheng, Gong, Lun, Zheng, Jian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Acceleration
Algorithms
Computed tomography
Computed tomography (CT)
Datasets
dynamic wavelet thresholding
graphics processing unit (GPU)
Graphics processing units
Heuristic algorithms
Image reconstruction
Interpolation
Iterative methods
Medical imaging
metal artifact reduction (MAR)
Metals
Quantitative analysis
Reduction
Reduction (metal working)
statistic iterative reconstruction (SIR)
Statistical analysis
Two dimensional models
Wavelet analysis
X-ray imaging
X-rays
ISSN:2469-7311, 2469-7303
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Popis
Shrnutí:The computed tomography (CT) imaging technique has been used to diagnose widespread disease for many years; however, severe streaking artifacts have been observed to appear in the reconstructed images of examinate contains metal objects. In this paper, we propose a dynamic wavelet thresholding metal artifact reduction (MAR) algorithm based on a statistic iterative reconstruction (SIR) model for 2-D fan beam CT. Cubic spline interpolation is utilized to remove blocky black artifacts caused by incomplete projections, and it also makes the solution closer to the optimum. The dynamic wavelet thresholding method contains the benefits of both wavelet soft and hard thresholding methods and promotes the sparsity of the image, which is used to erase residual streaking artifacts and accelerate the convergence of the SIR. The algorithm is accelerated by graphics processing unit programming and costs 14.44s with 40 iterations, meeting the demand of clinical practice. The performance of the proposed algorithm is compared with two classical MAR algorithms: the total variation (TV) constraint SIR algorithm and the reweighted TV constraint SIR algorithm. The experimental datasets include nine artificial datasets. Both qualitative and quantitative evaluation results show the outstanding performance of the proposed algorithm in suppressing the metal artifacts and preserving the image details.
Bibliografia:ObjectType-Article-1
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ISSN:2469-7311
2469-7303
DOI:10.1109/TRPMS.2017.2776970