A fast-iterative reconstruction algorithm for sparse angle CT based on compressed sensing

When conducting computed tomography (CT) image reconstruction, the existing algorithms do not analyze the physical and mathematical bases of CT image reconstruction, which leads to the lack of detailed information of reconstructed images and the problem of poor reconstruction effect. In this research, compressed sensing theory is applied in the process of fast-iterative CT reconstruction. The physical and mathematical basis for CT reconstruction is analyzed. A fast-iterative reconstruction algorithm for sparse angle CT images is proposed. Combined with algebraic iterative algorithm and gradient total variation minimization algorithm, CT reconstruction problem is transformed into gradient optimal solution problem, to complete the fast-iterative reconstruction of sparse angle CT images. Experimental results show that the proposed algorithm has high signal-to-noise ratio for CT image reconstruction, small reconstruction error, and good reconstruction effect for sparse angle CT images. • Compressed sensing theory is applied for fast-iterative CT reconstruction. • The physical and mathematical basis for CT reconstruction is analyzed. • A fast-iterative reconstruction algorithm for sparse angle CT images is proposed. • CT reconstruction problem is transformed into gradient optimal solution problem.