CT technology has been widely used in various fields such as medical treatment, industry, and materials. Recently years, cone-beam CT (CBCT) used in the medical field is replacing the traditional spiral CT slowly due to its unique advantages. Improving the performance of CBCT and the image reconstruction algorithms could obtain higher quality of images, meanwhile, these also reduce the exposure time of X-ray irradiation. And image reconstruction techniques based on sparse angles have benefits for both. This article briefly introduces the advantages of CBCT and the shortcomings of spiral CT, then the traditional filtered projection algorithm Feldkamp is explained. The development of the CBCT reconstruction algorithms based on incomplete data is analyzed from three aspects: TV model, dictionary learning and compressed sensing sampling. The advantages and disadvantages of these algorithms are analyzed for the development of new algorithms.
Cite this paper
Liu, X. , Huang, Y. and Luo, R. (2020). Development of Sparse Reconstruction Algorithm of Cone-Beam CT. Open Access Library Journal, 7, e6675. doi: http://dx.doi.org/10.4236/oalib.1106675.
Xiao, H., Gao, L.F., Yao, J., et al. (2014) Application of Cone Beam Computed Tomography and Its Quality Control Method. China Medical Devices, 29, 66-70.
Yan, B., Han, Y., Wei, F., et al. (2013) Review of Algorithms for over FOV Size Object in Cone-Beam CT. Computerized Tomography Theory and Applications, 22, 373-384.
Kouris, K., Tuy, H., Lent, A., Herman, G.T. and Lewitt, R.M. (1982) Reconstruction from Sparsely Sampled Data by ART with Interpolated Rays. IEEE Transactions on Medical Imaging, 1, 161-167. https://doi.org/10.1109/TMI.1982.4307567
Han, M., Cheng, X. and Li, D.W. (2017) Fast 3D Reconstruction Algorithm of Multi-Resolution Cone Beam CT Image Based on Wavelet Transform. Journal of Electronics and Information Technology, 39, 2437-2441.
Gilboa, H., Zuck, A., Dagan, O., Vilensky, A., Breen, B.N., Taieb, A., Ready, S., et al. (2003) Medical Imaging with Mercuric Iodide Direct Digital Radiography Flat-Panel X-Ray Detectors. X-Ray and Gamma-Ray Detectors and Applications IV, Volume 4784. https://doi.org/10.1117/12.450503
Candes, E.J., Romberg, J. and Tao, T. (2006) Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transactions on Information Theory, 52, 489-509.
https://doi.org/10.1109/TIT.2005.862083
Sidky, E.Y., Kao, C.-M. and Pan, X.C. (2006) Accurate Image Reconstruction from Few-Views and Limited-Angle Data in Divergent-Beam CT. Journal of X-Ray Science and Technology, 14, 119-139.
Chen, Z., Jin, X., Li, L. and Wang, G. (2013) A Limited-Angle CT Reconstruction Method Based on Anisotropic TV Minimization. Physics in Medicine and Biology, 58, 2119-2141. https://doi.org/10.1088/0031-9155/58/7/2119
Liu, Y., Ma, J., Fan, Y. and Liang, Z. (2012) Adaptive-Weighted Total Variation Minimization for Sparse Data toward Low-Dose X-Ray Computed Tomography Image Reconstruction. Physics in Medicine and Biology, 57, 7923-7956.
https://doi.org/10.1088/0031-9155/57/23/7923
Bian, J., Wang, J., Han, X., Sidky, E.Y., Shao, L. and Pan, X. (2012) Optimization-Based Image Reconstruction from Sparse-View Data in Offset-Detector CBCT. Physics in Medicine and Biology, 58, 205-230.
https://doi.org/10.1088/0031-9155/58/2/205
Zhang, H., Wang, L., Yan, B., et al. (2013) Image Reconstruction Based on Total-Variation Minimization and Alternating Direction Method in Linear Scan Computed Tomography. Chinese Physics B, 22, Article ID: 078701.
https://doi.org/10.1088/1674-1056/22/7/078701
Hu, Y., Ongie, G., Ramani, S. and Jacob, M. (2014) Generalized Higher Degree Total Variation (HDTV) Regularization. IEEE Transactions on Image Processing, 23, 2423-2435. https://doi.org/10.1109/TIP.2014.2315156
Liu, Y., Liang, Z.R., Ma, J.H., Lu, H.B., Wang, K., Zhang, H. and Moore, W. (2014) Total Variation-Stokes Strategy for Sparse-View X-Ray CT Image Reconstruction. IEEE Transactions on Medical Imaging, 33, 749-763.
https://doi.org/10.1109/TMI.2013.2295738
Bredies, K., Kunisch, K. and Pock, T. (2010) Total Generalized Variation. SIAM Journal on Imaging Sciences, 3, 492-526. https://doi.org/10.1137/090769521
Guo, W., Qin, J. and Yin, W. (2014) A New Detail-Preserving Regularization Scheme. SIAM Journal on Imaging Sciences, 7, 1309-1334.
https://doi.org/10.1137/120904263
Yan, B., Zhang, W., Li, L., Zhang, H. and Wang, L. (2016) Quantitative Study on Exact Reconstruction Sampling Condition by Verifying Solution Uniqueness in Limited-View CT. Physica Medica, 32, 1321-1330.
https://doi.org/10.1016/j.ejmp.2016.07.094
Mallat, S.G. and Zhang, Z. (1993) Matching Pursuits with Time-Frequency Dictionaries. IEEE Transactions on Signal Processing, 41, 3397-3415.
https://doi.org/10.1109/78.258082
Engan, K., Aase, S.O. and Husoy, J.H. (1999) Frame Based Signal Compression Using Method of Optimal Directions (MOD). ISCAS’99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI, Vol. 4, 1-4.
Mairal, J., Bach, F. and Ponce, J. (2012) Task-Driven Dictionary Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 791-804.
https://doi.org/10.1109/TPAMI.2011.156
Xu, Q., Yu, H.Y., Mou, X.Q., Zhang, L., Hsieh, J. and Wang, G. (2012) Low-Dose X-Ray CT Reconstruction via Dictionary Learning. IEEE Transactions on Medical Imaging, 31, 1682-1697. https://doi.org/10.1109/TMI.2012.2195669
Zhao, B., Ding, H., Lu, Y., Wang, G., Zhao, J. and Molloi, S. (2012) Dual-Dictionary Learning-Based Iterative Image Reconstruction for Spectral Computed Tomography Application. Physics in Medicine and Biology, 57, 8217-8229.
https://doi.org/10.1088/0031-9155/57/24/8217
Zhao, K., Pan, J.X. and Kong, H.H. (2013) Incomplete Projection Reconstruction Algorithm Based on Dictionary Learning and Iterative Algorithm. Proceedings of the 13th Chinese Conference on Body Vision and Image Analysis, Taiyuan, 23-25 September 2013, 422.
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R. and Ashok, A. (2016) ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 449-458. https://doi.org/10.1109/CVPR.2016.55
Dave, A., et al. (2017) Compressive Image Recovery Using Recurrent Generative Model. 2017 IEEE International Conference on Image Processing, Beijing, 17-20 September 2017, 1702-1706. https://doi.org/10.1109/ICIP.2017.8296572