Research On Reconstruction Algorithms Of New Generation CT Based On Low Dose Field

As a medical assistant diagnosis equipment,computed tomography(CT)plays an important role in the diagnosis of various diseases.However,because of its large radiation intensity,it causes irreversible harm to human body,and leads to certain limitations in clinical application.A new generation of CT imaging equipment with low radiation dose is an emergent field.However,with the significant reduction of radiation dose,the signal-to-noise ratio of low dose CT projection data will be seriously decreased,and result in serious degradation and distortion of reconstructed images.Traditional image reconstruction techniques have been unable to meet the needs for low dose CT.Therefore,it is of great practical value to develop a fast and accurate reconstruction algorithm based on the characteristics of projection data of low dose CT.The work of this paper is to carry out the research of reconstruction algorithm with high precision and fast iteration reconstruction.The main work includes:1)The imaging principle of CT is described and analyzed.Based on the characteristics of projection data generated by low dose CT,the noise statistical model is built.The projection data of low dose CT obey Poisson distribution and are influenced by the Gaussian noise.On this basis,the projection data of low dose CT is simulated based on the model proposed in this paper.2)An adjustable dynamic adaptive(ADSA-OSEM)algorithm is proposed to increase imaging quality.Based on the statistical characteristics of low dose projection data,the iterative reconstruction algorithm is improved to adapt low dose CT data.The proposed method combines the variable subset strategy with the penalty function of constrained optimization,instead of the traditional OSEM method with fixed subset.Compared with the OSEM method and classical FBP method,the effectiveness and superiority of this method are verified.By adjusting the adaptive parameters flexibly,the results show that the method has fewer iterations and better reconstructed image quality,and can effectively suppress granular noise and stripe artifacts.3)Furthermore,an OSEM(Bregman-OSEM)method based on Bregman iteration regularization is proposed to optimize OSEM method.For the classical ill-posed inverse problem of CT image reconstruction,the existing regularization methods have been proved to be able to solve this problem to a certain extent.However,because their regular terms are fixed,their adaptability to low doses is not good.Bregman iteration makes use of the adaptive distance constraint function derived from Bregman distance,which can change the original fixed regular optimization into gradually changing regular iteration and make the solving process more refined.Before high frequency noise is introduced,the image can be converged to keep the smoothness and fine structure of the image.This paper compares Bregman-OSEM with classical FBP,OSEM and OSEM algorithms based on total variation regularization(TV-OSEM).The computer simulation results show that Bregman-OSEM can maintain the image accuracy and improve the signal-to-noise ratio of the reconstructed image.This algorithm is a very effective method for low dose CT image reconstruction.