9 March 2017 A new approach to solving the prior image constrained compressed sensing (PICCS) with applications in CT image reconstruction
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Abstract
Reduce does exposure in computed tomography (CT) scan has been received much attention in recent years. It is reasonable to reduce the number of projections for reducing does. However, conventional CT image reconstruction methods will lead to streaking artifact due to few-view data. Inspired by the theory of compressive sensing, the total variation minimization method was widely studied in the CT image reconstruction from few-view and limited-angle data. It takes full advantage of the sparsity in the image gradient magnitude. In this paper, we propose a general prior image constrained compressed sensing model and develop an efficient iterative algorithm to solve it. The main idea of our approach is to reformulate the optimization problem as an unconstrained optimization problem with the sum of two convex functions. Then we derive the iterative algorithm by use of the primal dual proximity method. The prior image is reconstructed by a conventional analytic algorithm such as filtered backprojection (FBP) or from a dynamic CT image sequences. We demonstrate the performance of the proposed iterative algorithm in a quite few-view projection data with just 3 percent of the reconstructed image size. The numerical simulation results show that the proposed reconstruction algorithm outperforms the commonly used total variation minimization method.
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Yuchao Tang, Yuchao Tang, Chunxiang Zong, Chunxiang Zong, } "A new approach to solving the prior image constrained compressed sensing (PICCS) with applications in CT image reconstruction", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322T (9 March 2017); doi: 10.1117/12.2253573; https://doi.org/10.1117/12.2253573
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