低劑量CT圖像質(zhì)量改善算法研究
發(fā)布時間：2018-11-29 12:41
【摘要】：X射線計算機斷層掃描(Computed Tomography,CT)技術(shù)在農(nóng)林業(yè)應用、工業(yè)無損檢測、材料學以及醫(yī)學診斷等領(lǐng)域發(fā)展迅速,特別是在臨床醫(yī)學領(lǐng)域發(fā)揮巨大作用。X射線輻射會對病患造成一定程度的傷害,并誘發(fā)癌癥等疾病,因此如何在獲取解剖信息清晰且密度分辨率高的重建圖像的同時盡可能地減少CT輻射劑量已成為CT研究者們的奮斗目標。降低管電流是一種減少輻射劑量的有效方法,此方法會導致投影數(shù)據(jù)產(chǎn)生噪聲,進而損失低劑量CT重建圖像的質(zhì)量。本文主要采用改進重建算法、對投影數(shù)據(jù)進行去噪以及對重建圖像的噪聲濾除三種方法進行噪聲去除和偽影抑制,主要創(chuàng)新工作如下:1.為克服全變分算法容易造成階梯偽影和過度平滑的問題,構(gòu)造一個加權(quán)方差和圖像梯度共同作用的邊緣指示函數(shù),該擴散函數(shù)與全變分(Total Variation,TV)模型結(jié)合得到基于加權(quán)方差的TV模型。進而,將新模型引入到懲罰加權(quán)最小二乘重建(Penalized Weighted Least Square,PWLS)算法中得到一種基于加權(quán)方差TV的統(tǒng)計迭代重建去噪算法。采用兩步進行新模型的優(yōu)化估計,首先,采用交替方向迭代法將聯(lián)合問題分解為兩個子問題,接著,分別采用梯度下降法和可分離拋物線替代法求解。通過視覺效果和量化指標分析,新算法重建圖像質(zhì)量在得到明顯改善的同時邊緣細節(jié)分辨率高。2.由于中值濾波不僅能夠消除脈沖噪聲,還可以較好地保留圖像邊緣,給出一種基于中值非局部先驗的最大后驗概率投影域濾波算法。該算法先對投影圖像進行中值濾波,再根據(jù)圖像塊間的相似性進行自適應非局部降噪,采用高斯-賽德爾方法得到所提模型的最優(yōu)解,最后經(jīng)過濾波反投影(Filtered Back Projection,FBP)算法得到最終CT重建圖像。采用修正后的大腦模型進行仿真實驗,所提算法不僅在平滑投影圖像噪聲和抑制條形偽影方面表現(xiàn)良好,且可獲得高信噪比圖像。3.直覺模糊熵(Intuition Fuzzy Entropy,IFE)能夠自適應地區(qū)分圖像平坦區(qū)域和邊緣細節(jié)區(qū)域,便將其與各項異性擴散模型的擴散函數(shù)共同作用得到一種基于IFE的邊緣擴散函數(shù)。同時,采用新指示函數(shù)對廣義全變分(Total Generalized Variation,TGV)模型改進得到新的自適應TGV正則化濾波模型。最后,通過一階原始-對偶算法求解新模型獲取最終重建圖像。仿真模型和實際數(shù)據(jù)實驗結(jié)果均表明新算法在抑制噪聲和去除條狀偽影方面表現(xiàn)突出,同時較好地保留低劑量CT復原圖像的紋理結(jié)構(gòu)特征。
[Abstract]:X-ray computed tomography (Computed Tomography,CT) technology is developing rapidly in the fields of agriculture and forestry, industrial nondestructive testing, materials science and medical diagnosis, etc. In particular, it plays an important role in clinical medicine. X-ray radiation can cause a certain degree of injury to patients and induce diseases such as cancer. Therefore, how to obtain the reconstructed image with clear anatomical information and high density resolution while minimizing the radiation dose of CT has become the goal of CT researchers. Reducing the tube current is an effective method to reduce the radiation dose. This method can cause the noise of the projection data, and then lose the quality of the low dose CT reconstruction image. This paper mainly adopts improved reconstruction algorithm, denoising projection data and noise filtering of reconstructed image for noise removal and artifact suppression. The main innovative work is as follows: 1. In order to overcome the problems of step artifacts and excessive smoothing caused by the total variation algorithm, a boundary indicator function with weighted variance and image gradient is constructed. The diffusion function is combined with the total variational (Total Variation,. TV) model combined with TV model based on weighted variance. Furthermore, the new model is introduced into the penalty weighted least square reconstruction (Penalized Weighted Least Square,PWLS) algorithm to obtain a statistical iterative reconstruction denoising algorithm based on weighted variance TV. The optimal estimation of the new model is carried out with two steps. Firstly, the joint problem is decomposed into two sub-problems by alternating direction iteration method, and then the gradient descent method and the separable parabola substitution method are used to solve the joint problem. Through visual effect and quantization index analysis, the reconstruction image quality of the new algorithm is improved obviously and the resolution of edge detail is high. 2. Because median filter can not only eliminate impulse noise, but also preserve image edge, a projection domain filtering algorithm based on median nonlocal priori is presented. The algorithm first carries on median filtering to the projection image, then adaptively non-local noise reduction according to the similarity between the image blocks. The optimal solution of the proposed model is obtained by using the Gauss-Seidel method. Finally, the filtered backprojection (Filtered Back Projection, is used. FBP) algorithm to get the final CT reconstruction image. The modified brain model is used for simulation experiments. The proposed algorithm not only performs well in smoothing projection image noise and suppressing bar artifact, but also can obtain high SNR image. Intuitionistic fuzzy entropy (Intuition Fuzzy Entropy,IFE) can self-adaptively distinguish the flat region from the edge detail region, and then work together with the diffusion functions of various anisotropic diffusion models to obtain an edge diffusion function based on IFE. At the same time, a new adaptive TGV regularization filter model is obtained by using a new indicator function to improve the generalized total variational (Total Generalized Variation,TGV) model. Finally, the first-order primitive-dual algorithm is used to solve the new model to obtain the final reconstructed image. Both the simulation model and the experimental results show that the new algorithm is very effective in noise suppression and strip artifact removal, while preserving the texture features of low dose CT reconstructed images.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2365023
[Abstract]:X-ray computed tomography (Computed Tomography,CT) technology is developing rapidly in the fields of agriculture and forestry, industrial nondestructive testing, materials science and medical diagnosis, etc. In particular, it plays an important role in clinical medicine. X-ray radiation can cause a certain degree of injury to patients and induce diseases such as cancer. Therefore, how to obtain the reconstructed image with clear anatomical information and high density resolution while minimizing the radiation dose of CT has become the goal of CT researchers. Reducing the tube current is an effective method to reduce the radiation dose. This method can cause the noise of the projection data, and then lose the quality of the low dose CT reconstruction image. This paper mainly adopts improved reconstruction algorithm, denoising projection data and noise filtering of reconstructed image for noise removal and artifact suppression. The main innovative work is as follows: 1. In order to overcome the problems of step artifacts and excessive smoothing caused by the total variation algorithm, a boundary indicator function with weighted variance and image gradient is constructed. The diffusion function is combined with the total variational (Total Variation,. TV) model combined with TV model based on weighted variance. Furthermore, the new model is introduced into the penalty weighted least square reconstruction (Penalized Weighted Least Square,PWLS) algorithm to obtain a statistical iterative reconstruction denoising algorithm based on weighted variance TV. The optimal estimation of the new model is carried out with two steps. Firstly, the joint problem is decomposed into two sub-problems by alternating direction iteration method, and then the gradient descent method and the separable parabola substitution method are used to solve the joint problem. Through visual effect and quantization index analysis, the reconstruction image quality of the new algorithm is improved obviously and the resolution of edge detail is high. 2. Because median filter can not only eliminate impulse noise, but also preserve image edge, a projection domain filtering algorithm based on median nonlocal priori is presented. The algorithm first carries on median filtering to the projection image, then adaptively non-local noise reduction according to the similarity between the image blocks. The optimal solution of the proposed model is obtained by using the Gauss-Seidel method. Finally, the filtered backprojection (Filtered Back Projection, is used. FBP) algorithm to get the final CT reconstruction image. The modified brain model is used for simulation experiments. The proposed algorithm not only performs well in smoothing projection image noise and suppressing bar artifact, but also can obtain high SNR image. Intuitionistic fuzzy entropy (Intuition Fuzzy Entropy,IFE) can self-adaptively distinguish the flat region from the edge detail region, and then work together with the diffusion functions of various anisotropic diffusion models to obtain an edge diffusion function based on IFE. At the same time, a new adaptive TGV regularization filter model is obtained by using a new indicator function to improve the generalized total variational (Total Generalized Variation,TGV) model. Finally, the first-order primitive-dual algorithm is used to solve the new model to obtain the final reconstructed image. Both the simulation model and the experimental results show that the new algorithm is very effective in noise suppression and strip artifact removal, while preserving the texture features of low dose CT reconstructed images.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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