Abstract
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.
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References
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. https://doi.org/10.1109/TSP.2006.881199
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. https://doi.org/10.1177/016173468400600107
Barzilai, J., Borwein, J., 1988. Two-point step size gradient methods. IMA J. Numer. Anal., 8(1):141–148. https://doi.org/10.1093/imanum/8.1.141
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. https://doi.org/10.1109/TIT.2005.862083
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. https://doi.org/10.1109/TMI.2014.2336860
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. https://doi.org/10.1109/TIP.2007.901238
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. https://doi.org/10.1109/TIP.2012.2235847
Elad, M., Aharon, M., 2006. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process., 15(12):3736–3745. https://doi.org/10.1109/TIP.2006.881969
Ginat, D.T., Gupta, R., 2014. Advances in computed tomography imaging technology. Ann. Rev. Biomed. Eng., 16:431–453. https://doi.org/10.1146/annurev-bioeng-121813-113601
Han, X., Bian, J.G., Ritman, E.L., et al., 2012. Optimizationbased reconstruction of sparse images from few-view projections. Phys. Med. Biol., 57(16):5245–5273. https://doi.org/10.1088/0031-9155/57/16/5245
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. https://doi.org/10.1088/0031-9155/56/13/004
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. https://doi.org/10.1109/ISBI.2016.7493371
Lustig, M., Donoho, D.L., Santos, J.M., et al., 2008. Compressed sensing MRI. IEEE Signal Process. Mag., 25(2):72–82. https://doi.org/10.1109/MSP.2007.914728
Niu, T.Y., Zhu, L., 2012. Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for conebeam CT: phantom studies. Med. Phys., 39(7):4588–4598. https://doi.org/10.1118/1.4729837
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. https://doi.org/10.1088/0031-9155/59/7/1801
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. https://doi.org/10.1118/1.3679865
Siddon, R.L., 1985. Prism representation: a 3D ray-tracing algorithm for radiotherapy application. Phys. Med. Biol., 30:817–824. https://doi.org/10.1088/0031-9155/30/8/005
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. https://doi.org/10.1088/0031-9155/53/17/021
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. https://doi.org/10.1109/TIP.2003.819861
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. https://doi.org/10.1109/TMI.2012.2195669
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. https://doi.org/10.1088/0031-9155/57/7/2063
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. https://doi.org/10.1118/1.4898324
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. https://doi.org/10.1088/0031-9155/55/13/022
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. https://doi.org/10.1631/FITEE.1400423
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Project supported by the National High-Tech R&D Program (863) of China (No. 2015AA016704c) and the Zhejiang Provincial Natural Science Foundation, China (No. LY14F020028)
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Ding, Y., Hu, T. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing. Frontiers Inf Technol Electronic Eng 18, 2001–2008 (2017). https://doi.org/10.1631/FITEE.1700287
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DOI: https://doi.org/10.1631/FITEE.1700287