Abstract
Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.
Highlights
Since computed tomography (CT) [1] technique was born in 1973, CT has been widely applied in medical diagnose, industrial nondestructive detection, and so forth
Sharp frequency localization Contourlet transform [4] is firstly proposed by Lu and Do in 2006 and Feng et al introduced a detailed explanation and construction in 2009 which is named as non-aliasing Contourlet transform (NACT) [5]
We propose a CT reconstruction algorithm based on NACT and compressive sensing which tries to explore the sparse capability of NACT in order to reconstruct high-quality CT images
Summary
Since computed tomography (CT) [1] technique was born in 1973, CT has been widely applied in medical diagnose, industrial nondestructive detection, and so forth. In medical CT field, how to reconstruct high-quality CT images from few-views or sparse-views data is a significant research problem. Compressive sensing (CS) [2] theory has been applied in CT images reconstruction which makes it possible to reconstruct high-quality images from few-views data. Contourlet transform can get important smooth contour features of the image with few data, but there is frequency aliasing in Contourlet transform. To solve the optimization problem in CT images reconstruction based on CS, Goldstein and Osher proposed SplitBregman [6] method, which is derived from Bregman [7] iteration and can accelerate iteration convergence and produce better reconstruction results. We propose a CT reconstruction algorithm based on NACT and compressive sensing which tries to explore the sparse capability of NACT in order to reconstruct high-quality CT images.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
