本文選題：低劑量CT　切入點(diǎn)：圖像重建　出處：《南方醫(yī)科大學(xué)》2017年碩士論文

【摘要】：計(jì)算機(jī)斷層成像(Computed Tomography,CT)是現(xiàn)代醫(yī)學(xué)診斷及放射治療的重要技術(shù)手段,然而有研究表明,過高劑量的X射線照射會危害受檢者健康。在保持現(xiàn)有硬件設(shè)備不變情況下,可通過稀疏角度及有限角度掃描來有效降低輻射劑量,但這兩種掃描方式皆因數(shù)據(jù)采樣不足而會使重建圖像質(zhì)量發(fā)生嚴(yán)重退化。因此,如何在降低輻射劑量的同時(shí)確保重建圖像質(zhì)量不退化已成為當(dāng)前放射成像領(lǐng)域關(guān)注的焦點(diǎn)。本文通過系統(tǒng)回顧C(jī)T成像理論基礎(chǔ)及對經(jīng)典重建算法進(jìn)行學(xué)習(xí)與掌握,針對低劑量CT成像中的稀疏角度及有限角度圖像重建問題展開深入研究,并提出以下三種重建算法:第一,針對非局部平均(Non-local Means,NLM)算法在稀疏角度重建的不足,改進(jìn)并實(shí)現(xiàn)了一種基于自適應(yīng)NLM約束的CT重建算法。該算法定義了一種新型相似窗,并設(shè)計(jì)一種基于相似窗旋轉(zhuǎn)變換的相似性測度,從而精確衡量像素間的相似性,避免圖像細(xì)節(jié)模糊。其次,設(shè)計(jì)了一種基于像素梯度大小及當(dāng)前迭代次數(shù)的自適應(yīng)濾波參數(shù),從而在抑制噪聲的同時(shí)銳化圖像邊緣。仿真及真實(shí)數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明,新算法可以有效消除噪聲與偽影并且保持圖像的邊緣特性。與傳統(tǒng)算法相比,信噪比提高了 38%,平均誤差則降低了 76%。第二,針對 NLM 約束的代數(shù)重建算法(Algebraic Reconstruction Technique,ART)在稀疏角度重建的過平滑問題,改進(jìn)并實(shí)現(xiàn)了一種基于旋轉(zhuǎn)不變性的自適應(yīng)NLM重建算法。該算法設(shè)計(jì)了一種基于旋轉(zhuǎn)不變性的相似性測度用于計(jì)算相似窗間的相似距離,與此同時(shí),根據(jù)像素所處位置特征及重建圖像的噪聲大小自適應(yīng)調(diào)整濾波參數(shù),從而避免圖像邊緣區(qū)域被過度平滑。仿真及真實(shí)數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明,本算法可以在平滑噪聲和偽影的同時(shí),還原圖像的邊緣結(jié)構(gòu)細(xì)節(jié)。與傳統(tǒng)ART-NLM算法相比,信噪比提高了 51%,平均絕對誤差降低了 74%。第三,針對有限角度CT圖像重建存在嚴(yán)重偽影問題,改進(jìn)并實(shí)現(xiàn)了一種基于局部和非局部正則化的有限角度CT圖像重建算法。通過改進(jìn)傳統(tǒng)NLM算法以利用非偽影區(qū)域正確的圖像信息來恢復(fù)偽影區(qū)域的像素值,然后改用全變差最小化方法校正由非偽影區(qū)域引入的偽結(jié)構(gòu)信息。仿真及真實(shí)數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明,本算法可以大幅度減少重建圖像的幾何失真?zhèn)斡?顯著提高圖像質(zhì)量。與ART-NLM算法相比,信噪比提高了 57%,平均絕對誤差則降低了 52%。本文針對稀疏角度及有限角度CT圖像重建問題,改進(jìn)并實(shí)現(xiàn)了三種重建算法,顯著提高了重建圖像質(zhì)量。雖然本文所研究的內(nèi)容在圖像精度上取得了一些初步成果,但對于在實(shí)際臨床應(yīng)用還有待更深一步的探究。
[Abstract]:Computed Tomography (CTT) is an important technique in modern medical diagnosis and radiotherapy. However, some studies have shown that high doses of X-ray irradiation can harm the health of the patients.Under the condition of keeping the existing hardware equipment unchanged, the radiation dose can be effectively reduced by sparse and limited angle scanning. However, the quality of reconstructed image can be seriously degraded by these two scanning methods due to insufficient data sampling.Therefore, how to reduce the radiation dose while ensuring that the reconstructed image quality does not degenerate has become the focus in the field of radiography.By reviewing the theoretical basis of CT imaging and learning and mastering the classical reconstruction algorithms, the sparse and finite angle image reconstruction problems in low dose CT imaging are studied in this paper.Three reconstruction algorithms are proposed as follows: first, a CT reconstruction algorithm based on adaptive NLM constraints is improved and implemented to overcome the deficiency of non-local average non-local mean mean (NLM) algorithm in sparse angle reconstruction.Secondly, an adaptive filtering parameter based on pixel gradient and the number of iterations is designed to reduce the noise and sharpen the edge of the image.Simulation and real data experiments show that the new algorithm can effectively eliminate noise and artifacts and preserve the edge characteristics of the image.Compared with the traditional algorithm, the SNR is increased by 38 and the average error is reduced by 76.Secondly, an adaptive NLM reconstruction algorithm based on rotation invariance is improved and implemented to solve the problem of over-smoothing in sparse angle reconstruction of Algebraic Reconstruction Technique ART (algebraic reconstruction algorithm) with NLM constraints.In this algorithm, a similarity measure based on rotation invariance is designed to calculate the similarity distance between similar windows. At the same time, the filtering parameters are adjusted adaptively according to the location characteristics of pixels and the noise size of the reconstructed image.Thus, the edge area of the image is not over-smoothed.Simulation and real data experiments show that the proposed algorithm can restore the edge structure details of the image while smoothing noise and artifacts.Compared with the traditional ART-NLM algorithm, the SNR is improved 51%, and the average absolute error is reduced 74%.Thirdly, aiming at the serious artifact problem in finite angle CT image reconstruction, an algorithm of limited angle CT image reconstruction based on local and non-local regularization is improved and implemented.The traditional NLM algorithm is improved to recover the pixel value of the artifact region by using the correct image information of the non-artifact region, and then the pseudo-structure information introduced by the non-artifact region is corrected by the method of total variation minimization.Simulation and real data experiments show that the proposed algorithm can greatly reduce the geometric distortion of reconstructed images and improve the image quality significantly.Compared with the ART-NLM algorithm, the SNR is increased by 57 and the mean absolute error is reduced by 522.In order to solve the problem of sparse and finite angle CT image reconstruction, three reconstruction algorithms are improved and implemented in this paper, and the quality of reconstructed images is improved significantly.Although some preliminary achievements have been made in the image accuracy of the content studied in this paper, it needs to be further explored in the practical clinical application.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R814.42;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 李毅;潘晉孝;;一種基于數(shù)據(jù)外插改進(jìn)的ART迭代算法[J];CT理論與應(yīng)用研究;2011年01期

2 金鑫;李亮;陳志強(qiáng);張麗;邢宇翔;;Motion-Compensated Reconstruction Method Based on Rigid Motion Model with Multi-Object[J];Tsinghua Science and Technology;2010年01期

3 王革;俞恒永;勃魯努·德·曼;莊天戈;趙俊;;X線CT研究與發(fā)展之展望[J];中國醫(yī)療器械雜志;2008年03期

，

本文編號：1695496