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CLC number: TP391.41

On-line Access: 2018-02-06

Received: 2017-04-30

Revision Accepted: 2017-08-09

Crosschecked: 2017-12-22

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Citations:  Bibtex RefMan EndNote GB/T7714


Yong Ding


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.12 P.2001-2008


Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing

Author(s):  Yong Ding, Tuo Hu

Affiliation(s):  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   dingy@vlsi.zju.edu.cn

Key Words:  Low-dose computed tomography (CT), CT imaging, Total variation, Sparse dictionary learning

Yong Ding, Tuo Hu. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2001-2008.

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%T Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing
%A Yong Ding
%A Tuo Hu
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T1 - Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing
A1 - Yong Ding
A1 - Tuo Hu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 12
SP - 2001
EP - 2008
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Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700287

Recently, low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative reconstruction has achieved excellent imaging performance, but its clinical application is hindered due to its computational inefficiency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation minimization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.


概要:随着CT(computed tomography)中过量辐射剂量带来的健康风险日渐引发人们的担忧,低剂量CT得到了大量的关注。然而对于低剂量CT成像而言,在降低剂量的同时保证图像的高质量是一个很大的挑战。相比传统的滤波反投影算法,基于压缩感知的迭代重建法取得了良好的成像效果。但是迭代重建计算复杂度高,阻碍了其临床应用。本文提出一种结合全变分(total variation, TV)最小化和稀疏字典学习的重建方法,不仅提高了重建效果,而且通过自适应的停止策略提高了重建速度。实验结果表明,本文提出的方法相比其他类型的方法能获得更好的图像质量和更高的计算效率。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Aharon, M., Elad, M., Bruckstein, A.M., 2006. The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process., 54(11):4311-4322.

[2]Anderson, A.H., Kak, A.C., 1984. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrason. Imag., 6(1):81-94.

[3]Barzilai, J., Borwein, J., 1988. Two-point step size gradient methods. IMA J. Numer. Anal., 8(1):141-148.

[4]Candes, E.J., Romberg, J., Tao, T., 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52(2):489-509.

[5]Chen, Y., Shi, L.Y., Feng, Q.J., et al., 2014. Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans. Med. Imag., 33(12):2271-2292.

[6]Dabov, K., Foi, A., Katkovnik, V., et al., 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., 16(8):2080-2095.

[7]Dong, W.S., Zhang, L., Shi, G.M., et al., 2013. Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process., 22(4):1620-1630.

[8]Elad, M., Aharon, M., 2006. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process., 15(12):3736-3745.

[9]Ginat, D.T., Gupta, R., 2014. Advances in computed tomography imaging technology. Ann. Rev. Biomed. Eng., 16:431-453.

[10]Han, X., Bian, J.G., Ritman, E.L., et al., 2012. Optimization-based reconstruction of sparse images from few-view projections. Phys. Med. Biol., 57(16):5245-5273.

[11]Jia, X., Dong, B., Lou, Y.F., et al., 2011. GPU-based iterative cone-beam CT reconstruction using tight frame regularization. Phys. Med. Biol., 56:3787-3807.

[12]Liu, J., Chen, Y., Hu, Y., et al., 2016. Low-dose CBCT reconstruction via 3D dictionary learning. IEEE 13th Int. Symp. on Biomedical Imaging, p.735-738.

[13]Lustig, M., Donoho, D.L., Santos, J.M., et al., 2008. Compressed sensing MRI. IEEE Signal Process. Mag., 25(2): 72-82.

[14]Niu, T.Y., Zhu, L., 2012. Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: phantom studies. Med. Phys., 39(7):4588-4598.

[15]Niu, T.Y., Ye, X.J., Fruhauf, Q., et al., 2014. Accelerated barrier optimization compressed sensing (ABOCS) for CT reconstruction with improved convergence. Phys. Med. Biol., 59(7):1801-1814.

[16]Park, J.C., Song, B.Y., Kim, J.S., et al., 2012. Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT. Med. Phys., 39(3):1207-1217.

[17]Siddon, R.L., 1985. Prism representation: a 3D ray-tracing algorithm for radiotherapy application. Phys. Med. Biol., 30:817-824.

[18]Sidky, E.Y., Kao, C.M., Pan, X.C., 2008. Image reconstruction in circular cone-beam computed tomogrphy by constrained, total-variation minimization. Phys. Med. Biol., 53:4777-4807.

[19]Wang, Z., Bovik, A.C., Sheikh, H.R., et al., 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612.

[20]Xu, Q., Yu, H.Y., Mou, X.Q., et al., 2012. Low dose X-ray reconstruciton via dictionary learning. IEEE Trans. Med. Imag., 31(9):1682-1697.

[21]Yan, H., Cervino, L., Jia, X., et al., 2012. A comprehensive study on the relationship between the image quality and imaging dose in low-dose cone beam CT. Phys. Med. Biol., 57(7):2063-2080.

[22]Yan, H., Wang, X.Y., Shi, F., et al., 2014. Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: cone/ring artifact correction and multiple GPU implementation. Med. Phys., 41(11):1-15.

[23]Yu, H.Y., Wang, G., 2010. A soft-threshold filtering approach for reconstruction from a limited number of projections. Phys. Med. Biol., 55:3905-3916.

[24]Yuan, M., Yang, B.X., Ma, Y.D., et al., 2015. Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm. Front. Inform. Technol. Electron. Eng., 16(2):1069-1087.

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