Compressing-sensing cone-beam CT short-scan reconstruction based on projection-contraction

To solve the problem of image reconstruction of incomplete projection data from a short-scan cone-beam CT, a novel cone-beam CT short-scan reconstruction algorithm was proposed based on projection-contraction method. Aiming at the non-monotonic convergence of the Gradient-Projection Barzilari-Borwein (GPBB) algorithm, the predictor-corrector feature of projection-contraction method was analyzed and incorporated into compressed sensing image reconstruction algorithm. The objective function descent direction and the projection onto convex sets descent direction were combined to correct the results of GPBB algorithm to improve the non-monotonic convergence of GPBB algorithm. Then, the experiments were conducted on simulated projection data and phantom scanning data. The simulated results for 25 sampling angles show that the signal-to-noise ratios of images reconstructed by PCBB algorithm are 9.4870, 9.8027, 3.6159 db higher than those of images reconstructed by Adaptive Steepest Descent-Projection onto Convex Sets algorithm, projection contraction algorithm and GPBB algorithm, respectively. The simulation results indicate that when a small amount of projections are acquired, the new algorithm has effectively suppressed strip artifacts and the reconstructed images show clear edges. The algorithm can greatly improved the qualify of images reconstructed from few projection data.