本文選題：低劑量CT + 圖像重建��； 參考：《東北大學》2015年博士論文

【摘要】：CT是疾病診療過程中常用的設備之一,在疾病的診斷中無法用其它設備取代CT。但作為一種放射型設備,CT的廣泛使用增加了患者吸收的X射線劑量,對身體健康造成潛在的危害。由于低劑量CT可以減少X射線對患者的傷害,因此在不影響重建圖像質(zhì)量的前提下,如何降低X射線劑量受到越來越多的關注和研究。通常,可以通過減少掃描角度、減少掃描時間和降低放射電流三種方式來降低放射的劑量。掃描角度的減少會帶來投影數(shù)據(jù)的不完備,通過迭代重建算法可以很好的解決這種數(shù)據(jù)不完備問題；掃描時間的減少或放射電流的降低使得光子數(shù)減少,從而引起投影數(shù)據(jù)的噪聲過大,進而導致重建后的圖像含有嚴重的高斯噪聲,基于圖像域和投影域的去噪方法可以有效的抑制噪聲。此外,對帶有金屬植入體的患者進行掃描時,重建后的CT圖像內(nèi)部會產(chǎn)生嚴重的金屬偽影,如何精確地恢復出被金屬影響的投影數(shù)據(jù),是一個很有意義的問題。最后探討的是因探測器單元的異常而引起圖像中產(chǎn)生環(huán)狀偽影的問題,通過投影域濾波的方法能夠有效地去除環(huán)狀偽影。因此,本論文主要圍繞低劑量CT的圖像重建和偽影的校正過程中的關鍵點和難點進行了如下的研究：(1)針對如何從不完備的投影數(shù)據(jù)重建出高質(zhì)量CT圖像的問題,提出了一種有序子集重建算法。該算法利用投影到凸集合加快重建的收斂速率,結合全變分最小化和快速一階方法來減少重建的迭代次數(shù),采用分裂Bregman交替方向法求解優(yōu)化問題。通過對仿真數(shù)據(jù)和實際數(shù)據(jù)進行實驗,驗證了提出的方法在不完備投影數(shù)據(jù)情況下的重建效果,為算法的實際應用提供了理論和實驗基礎。(2)考慮CT圖像的高斯噪聲問題,提出了一種基于CUDA加速的三維全變分最小化算法。該算法自動計算迭代步長,利用梯度下降法減少圖像的全變分。通過對仿真和實際數(shù)據(jù)的實驗結果進行分析,驗證了提出的加速方法在保持圖像的紋理和邊界信息的前提下,可以有效地降低計算時間,減少大部分的高斯噪聲。(3)針對抑制投影數(shù)據(jù)低劑量噪聲和去噪模型的參數(shù)選擇問題,根據(jù)投影域去噪的階段不同,分別提出了基于對數(shù)前和對數(shù)后的四種去噪算法。在對數(shù)前的投影去噪中,提出基于一維投影升維的自適應步長梯度下降法、基于二維投影數(shù)據(jù)的自適應步長梯度下降法和自動計算懲罰參數(shù)的乘子交替方向法。在對數(shù)后的投影數(shù)據(jù)去噪過程中,針對對數(shù)后的噪聲模型,提出一種自動計算噪聲方差的去噪算法。該算法利用方差穩(wěn)定性變換將信號依賴的高斯噪聲轉(zhuǎn)換為獨立高斯噪聲,通過噪聲方差評估的方法確定去噪?yún)?shù),采用三維濾波塊匹配法和基于參考圖像的非局部均值法進行去噪。通過仿真和實際數(shù)據(jù)的實驗,驗證了本論文提出的四個算法的可行性和有效性。(4)為了去除圖像中的金屬偽影,提出了一種基于冗余表達的校正方法。首先,分割出CT圖像中的金屬區(qū)域,將其投影得到金屬投影數(shù)據(jù)；然后,從原始投影數(shù)據(jù)中去除金屬部分的投影數(shù)據(jù)；最后,利用冗余表達的算法恢復金屬部分的投影數(shù)據(jù),經(jīng)過濾波反投影后,重建出校正后的圖像。校正結果顯示,該算法可以有效地減少重建圖像的金屬偽影。(5)為了降低圖像中的環(huán)狀偽影,提出了一種基于CUDA加速的環(huán)狀偽影校正算法。該算法利用GPU加速二維投影數(shù)據(jù)的中值濾波過程。校正結果顯示,與CPU處理速度相比,提出的算法在處理時間上明顯更快,在消除了環(huán)狀偽影的同時,保持了圖像的空間分辨率。
[Abstract]:CT is one of the commonly used equipment in the process of diagnosis and treatment of disease. CT. can not be replaced by other equipment in the diagnosis of disease, but as a radioactive device, the extensive use of CT increases the dose of X ray absorbed by the patient, causing potential harm to health. Because low dose CT can reduce the harm of X ray to the patient, so it does not affect the weight of the patient. On the premise of building the image quality, how to reduce the dose of X ray is paid more and more attention and research. Usually, the dose of radiation can be reduced by reducing the scanning angle, reducing the scanning time and reducing the radiation current. The decrease of the scanning angle will bring the incomplete projection data, and the iterative reconstruction algorithm can be very good by the iterative reconstruction algorithm. To solve the problem of incomplete data, the reduction of scanning time or the decrease of the radiate current makes the number of photons reduce, which causes the noise of the projection data to be too large, and then the reconstructed image contains serious Gauss noise. The denoising method based on the image domain and the projection domain can effectively suppress the noise. When the patient is scanned, a serious metal artifact can be produced inside the reconstructed CT image. How to accurately restore the projected data affected by the metal is a very meaningful problem. Finally, the problem of the annular artifact in the image is caused by the anomaly of the detector unit, which can be filtered by the projection domain. Therefore, this paper focuses on the key and difficult points in the process of image reconstruction and artifact correction in low dose CT. (1) an ordered subset reconstruction algorithm is proposed for the problem of how to reconstruct high quality CT images from incomplete projection data. The convex set speeds up the convergence rate of the reconstruction, combines the total variation minimization and the fast first order method to reduce the iteration number of the reconstruction. The split Bregman alternating direction method is used to solve the optimization problem. The simulation data and the actual data are tested to verify the reconstruction effect of the proposed method in the case of not finished projection data, which is an algorithm. The practical application provides the theoretical and experimental basis. (2) considering the Gauss noise problem of CT images, a three dimensional total variational minimization algorithm based on CUDA acceleration is proposed. The algorithm automatically calculates the iteration step and reduces the total variation of the image by the gradient descent method. The results of the simulation and actual data are analyzed and verified. The proposed acceleration method can effectively reduce the computation time and reduce most of the Gauss noise on the premise of maintaining the texture and boundary information of the image. (3) according to the parameter selection of low dose noise and de-noising model for suppressing the projection data, according to the difference of the order of the denoising in the projection domain, the method is proposed based on the logarithm before and after the logarithm respectively. Four denoising algorithms. In the projection denoising before the logarithm, the adaptive step gradient descent method based on one dimension projection ascending dimension is proposed, the adaptive step gradient descent method based on the two-dimensional projection data and the multiplier alternate direction method are used to automatically calculate the penalty parameters. In the process of logarithmic projection data denoising, the logarithmic noise model is used. A denoising algorithm for automatic calculation of noise variance is proposed. The algorithm uses variance stability transform to transform the signal dependent Gauss noise into independent Gauss noise. The denoising parameters are determined by the method of noise variance evaluation. The denoising is carried out by the three-dimensional filter block matching method and the non local mean method based on the reference image. The experiment of actual data proves the feasibility and effectiveness of the four algorithms proposed in this paper. (4) in order to remove the metal artifacts in the image, a correction method based on redundant expression is proposed. First, the metal region in the CT image is separated and projected to the metal projection data; then, gold is removed from the original projection data. The projection data belongs to the part; finally, the projected data of the metal part is restored by the redundant expression algorithm. After the filtered back projection, the corrected image is reconstructed. The correction results show that the algorithm can effectively reduce the metal artifacts of the reconstructed image. (5) to reduce the ring artifact in the image, a CUDA acceleration based on the algorithm is proposed. The algorithm uses GPU to accelerate the median filtering process of the two-dimensional projection data. The correction results show that compared with the CPU processing speed, the proposed algorithm is significantly faster in processing time, while maintaining the spatial resolution of the image while eliminating the ring artifact.

【學位授予單位】：東北大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R814.42;TP391.41

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