The Image Reconstruction Of Limited Projection CT Based On Adaptive Iteration

X-ray computed tomography(CT)is an imaging technique widely used for medical diagnosis and treatments.Due to potential risk of inducing secondary cancers,it is desirable to reduce radiation doses of X-ray CT imaging.For CT reconstruction with limited data,iterative reconstruction(IR)methods have demonstrated their capability of producing high quality images.In this paper,reconstruction algotithm with limited projection data is mainly studied.Main contents are presented as follow:(1)In this work,we propose two adaptive iterative reconstruction algorithms for sparse-view X-ray computed tomography(CT).Treating the reconstruction problems as data fidelity constrained total variation(TV)minimization,both algorithms adapt the alternate two-stage strategy: projection onto convex sets(POCS)for data fidelity and non-negativity constraints and steepest descent for TV minimization.The novelty of this work is to determine iterative parameters automatically from data,thus avoiding tedious manual parameter tuning.InTVminimization,the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain,while the step size of algebraic reconstruction technique(ART)in POCS is determined based on the data noise level.In addition,projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs.The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data.(2)In this work,CT reconstruction was treated as a penalized weighted leastsquares optimization with the consideration of photon statistics of X-ray detection and converted it to a constrained total variation(TV)minimization.This constrained optimizationwas solved by alternate TV minimization through the first order primaldual(FOPD)algorithm and enforcement of the constraints of data fidelity and nonnegativity through projection onto convex sets(POCS).The proposed FOPD-POCS algorithm implemented an implicit balance mechanism between FOPD and POCS,which starts from strong POCS and weak FOPD TV minimization to quickly reach the feasible region,and then reinforces FOPD by skipping POCS steps given that TV solution satisfies the data fidelity constraint.The FOPD-POCS algorithm is compared with the classical adaptive steepest descent POCS(ASD-POCS)method for reconstruction performance and with a convergent simultaneous FOPD algorithm(“Sidkey-A7”)for reconstruction stability.The results demonstrate that FOPD–POCS can provide an easily controlled reconstruction to avoid complicated parameter tuning and to yield satisfying images in a short time.