A stochastic iterative evolution CT reconstruction algorithm for limited-angle sparse projection data

Abstract

Subject to data acquisition time, X-ray dose and geometric position of medical tomography system, only limited-angle or sparse projection data can be collected in many applications of medical computed tomography (CT). Lacking in completeness and symmetry of limited-angle sparse projection data leads to a huge search space for reconstruction algorithms. The conventional convex optimization methods typically suffer from poor convergence and unwanted local optima caused by the data insufficiency and the fixed descent path of iteration. To improve the reconstruction quality, a stochastic iterative evolution CT reconstruction algorithm is proposed, in which a population of solutions based on stochastic strategy is adopted to enhance the global search capability and the search direction is generated by combining gradient descent with fitness evaluation of stochastic population. Meanwhile, Markov Chain is introduced to predict the iterative evolution model and accelerate the proposed algorithm's convergence. Experiments results demonstrate the proposed algorithm's effectiveness and robustness in image reconstruction from limited-angle sparse projection data.