A Regularization Method for Computed Tomographic Reconstruction
The problems arising in the computed tomography-area are well known for their high dimensions and illposedness.Tikhonov regularization method is used to reconstruct parameter distribution from projection data.The linear programming and conjugate gradient method are used to compute the regularized solution for the least-square equations.In numerical simulation,the regularization method with linear programming was unable to reconstruct distributions effectively,while the approach based on conjugate gradient method produced reliable asymmetrical reconstructions by computing underdetermined equations and overdetermined equations respectively.The average errors using conjugate gradient regularization method were 2％ and the maximum value errors were 5％ after 10 iterations,which provided a good indication of the precision and convergence of the method.
computed tomography regularization method conjugate gradient method linear programming
Bin Zhang Yan He
College of Electromechanical Engineering,Qingdao University of Science and Technology,Qingdao,Shandong 266061,PR China
国际会议
太原
英文
660-663
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）