Effects of Data Correlation in a Sparse-View Compressive Sensing Based Image Reconstruction

Computed tomography and laminography are heavily investigated in a compressive sensing based image reconstruction framework to reduce the dose to the patients as well as to the radiosensitive devices such as multilayer microelectronic circuit boards. Nowadays researchers are actively working on optimizing the compressive sensing based iterative image reconstruction algorithm to obtain better quality images. However, the effects of the sampled data’s properties on reconstructed the image’s quality, particularly in an insufficient sampled data conditions have not been explored in computed laminography. In this paper, we investigated the effects of two data properties i.e. sampling density and data incoherence on the reconstructed image obtained by conventional computed laminography and a recently proposed method called spherical sinusoidal scanning scheme. We have found that in a compressive sensing based image reconstruction framework, the image quality mainly depends upon the data incoherence when the data is uniformly sampled.

• Sajid Abbas
• Joon Pyo Hong
• Jung-Ryun Lee
• Seungryong Cho

• Computed tomography
• Computed laminography
• Compressive sending
• Low-dose.

[1] S. Abbas, J. Min, and S. Cho, "Super-sparsely view-sampled cone-beam CT by incorporating prior data,” J. X-Ray Sci. Technol., vol. 21, pp. 71–83 (2013).
[2] S. Abbas, M. Park, J. Min, H. K. Kim, and S. Cho, "Sparse-view computed laminography with a spherical sinusoidal scan for nondestructive testing," Opt. Express (Accepted: 24 June,2014).
[3] E.Y. Sidky, C.-M. Kao, and X. Pan, "Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. X-Ray Sci. Tech., vol. 14, pp. 119-139 (2006).
[4] S. Cho, T. Lee, J. Min, and H. Chung, "Feasibility study on many-view under sampling (MVUS) technique for low-dose computed tomography,” Opt. Eng., vol. 51, pp. 080501 (2012).
[5] E. Y. Sidky, J. H. Jørgensen, and X. Pan, "Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm,” Phys. Med. Biol., vol. 57, pp. 3065-3091, 2012.
[6] J. C. Park, B. Song, J. S. Kim, H. K. Kim, Z. Liu, T. S. Suh, and W. Y. Song, "Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT," Med. Phys., vol. 30, pp. 1207- 121, 2012.
[7] T. Niu and L. Zhu, "Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: Phantom studies," Med. Phys., vol. 39, pp. 4588-4598, 2012.
[8] S. Abbas, T. Lee, S. Shin, R. Lee, and S. Cho, "Effects of sparse sampling schemes on image quality in low-dose CT,” Med. Phys. vol. 40, pp. 111915 (2013).
[9] E.Y. Sidky and X. Pan, "Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. vol. 53, pp. 4777-4807 (2008).
[10] F. Xu, L. Helfen, T. Baumbach, and H. Suhonen, "Comparison of image quality in computed laminography and tomography,” Opt. Express vol. 20 , pp. 794-806 (2012).
[11] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. vol. 13, pp. 600-612 (2004).