The Study Of Projection Sorting And Subset Selection In Iterative Reconstruction Algorithm Based On Cone-beam CT

In the CT image reconstruction, compared with the commonly used analytic method, iterative algorithm has strong anti-noise ability, but its calculation is complex, and the speed of reconstruction is slow. The order of applied projections and the selection of subset in the cone beam iterative algorithm have a great effect on speed of convergence and accuracy in reconstructed images. Taking applied projections sequence and order subset selection as research object, this paper proceeds to study. The mainly studying content of the paper follows aspects:(1) Projection applying sequence of algebraic reconstruction technique (ART) has effect on the convergence. In the mode of cone-beam scan and circular track, the correlation between ray-projections is analyzed. Projection angle sort scheme and projection applying sort scheme on a projection angle are given. The results show that:Projection angle sort scheme and projection applying sort scheme on a projection angle can all improve the convergence speed and image quality, but projection angle sort scheme the results of improving the convergence speed and image quality are obvious. Projection angle sort scheme is used to the helical trajectory and circular arc changing axis of rotation trajectory and good results also are reconstructed.(2) The correlation between ray-projections leads to the reduction of amount of the statistics information that the subset contains. In order to make amount of statistics information the subset contains maximum, this paper, firstly, sorts projections by the Weight Distance Scheme (WDS) and the Subset-level Interval-projection Scheme (SIS), to make the correlation between ray-projections as small as possible, and then projections are evenly distributed in the subsets. Simulation and actual experimental results show that:the method that projections are distributed in subsets after projections sort can improve convergence speed of the order subset algorithm and quality of images.(3) When Projection data has high noise level, grayscale in the smooth low-frequency region of the reconstructed images will become higher frequency, and the grayscale fluctuation becomes larger. The sub-region subset algorithm is proposed. This algorithm selects LAPLACE edge detection operator seriously affected by noise to separate reconstruction images. The small level subset is selected in high-frequency region and the large level subset is selected in low-frequency region, and then images are being reconstructed. Simulation and actual experimental results show that:in the case of large noise level, this algorithm has the same ability of noise suppression with the small level subset, also maintains the convergence rate of the large level subset, and the great quality actual images are reconstructs.