Research On CT Image Sparse Reconstruction Method Based On Relative TV

Computed Tomography(CT)technology is an important medical imaging technology,which plays an important role in accurate diagnosis and treatment.Among the mainstream image reconstruction algorithms of commercial CT system is the analytical method represented by filtered back projection algorithm,which has fast reconstruction speed and high imaging accuracy,but needs to use complete projection data.In order to reduce the radiation of ray to human body during scanning,an effective way is to only collect the projection under sparse angle.However,the image reconstructed from sparse projection using analytical method often contains serious strip artifacts,which affects its subsequent processing effect.The total variation(TV)minimum algorithm is a high-precision CT image sparse reconstruction algorithm.However,in some cases,the TV algorithm will produce stepped artifacts.One of the reasons may be that the total variation of the image is the norm of the image gradient.In the process of image TV minimization,the gradient at each pixel in the reconstructed image is punished with similar intensity.The research shows that in the field of image denoising,relative TV can use an ingenious windowed inherent variation(WIV)to carry out different adaptive penalties for image gradients of different sizes,which shows better denoising performance than TV algorithm.In view of this,this paper introduces relative TV into CT image sparse reconstruction to explore its sparse reconstruction capability.The main work of this paper is as follows:(1)A method of CT image sparse reconstruction with relative TV minimization is proposed.The constrained relative TV minimum model and its adaptive Steepest DescentProjection on Convex Sets(ASD-POCS)algorithm are designed.Reconstruction experiments were carried out with Shepp-Logan,FORBILD and real CT image simulation phantom to verify the correctness of the algorithm and evaluate the sparse reconstruction ability and anti-noise ability of the algorithm.The experimental results show that relative TV can achieve higher sparse reconstruction accuracy than TV algorithm.(2)Although the TV algorithm sometimes introduces step artifacts,its edge-preserving ability is strong.Compared with TV algorithm,it can better suppress step artifacts,but its edge-preserving ability is weak.For this reason,this paper proposes a joint minimum sparse reconstruction method of TV and relative TV.The joint minimum model of constraints and its ASD-POCS algorithm are designed.Reconstruction experiments were carried out with Shepp-Logan,FORBILD and real CT image simulation phantom to verify the correctness of the algorithm and evaluate the sparse reconstruction ability and anti-noise ability of the algorithm.Experimental results show that this algorithm can achieve higher reconstruction accuracy than the relative TV minimum algorithm.