本文選題：去卷積 + PCT成像��； 參考：《南方醫(yī)科大學》2017年碩士論文

【摘要】：急性缺血性腦卒中(acute ischemic stroke,AIS),又名急性腦梗死,是臨床上最為常見的腦血管疾病,這種腦循環(huán)障礙導致的局限性或者全面性神經(jīng)功能缺損綜合征,發(fā)病率高,是我國老年人致死、致殘的重要原因。腦灌注CT(perfusion CT,PCT)是臨床上廣泛用于AIS患者的檢查手段,但其對病變區(qū)域的連續(xù)動態(tài)掃描使患者承受極大的劑量輻射風險,甚至會誘發(fā)一些遺傳疾病。如何降低掃描劑量的同時不影響診斷圖像質量是當前CT領域亟待攻克的難題。當前,降低CT輻射劑量的方式有很多,在滿足臨床診斷需要的前提下盡量降低毫安-秒(mAs)是一個簡單且經(jīng)濟有效的方法。但是降低mAs,探測器采集的光子數(shù)急劇減少,使得投影數(shù)據(jù)被大量的光子噪聲污染,重建圖像質量也隨之嚴重受損,在圖像中表現(xiàn)為大量的噪聲和偽影。針對這種低mAs技術,為獲取最后的高質量參數(shù)圖,我們可由投影數(shù)據(jù)著手,恢復受噪聲污染的弦圖數(shù)據(jù),或者使用迭代重建的替代方式重建高質量的PCT序列圖像;也可以從PCT序列圖像本身著手,設計各類濾波器進行圖像后處理來恢復高質量PCT序列圖像;甚至可以在去卷積估計定量參數(shù)中引入合適的正則化形式,直接獲取臨床診斷可用的血流灌注參數(shù)圖。根據(jù)大量實驗數(shù)據(jù)統(tǒng)計分析,CT投影數(shù)據(jù)具有一定的統(tǒng)計特性。根據(jù)投影數(shù)據(jù)的統(tǒng)計特性,建立相應的去噪模型,從源頭上更好地去除噪聲。另一方面,在初步重建的低劑量PCT序列圖像中,我們可以觀測到序列圖像有著相同的背景信息,這部分固定的解剖結構在組織中對比劑濃度隨時間變化的過程中基本保持不變,因此,我們可將PCT序列圖像分為背景和增強部分來看待。觀測某塊組織灌注信息像素點CT值得變化(增強情況),不僅與單個像素點的CT值相關,更確切地說是一種區(qū)域效果,而非單個像素點小效果。將圖像看作許多塊來處理,更具有實際意義。根據(jù)以上信息,將主要工作歸納如下:(1)提出一種基于懲罰加權最小二乘(penalized weighted least-square,PWLS)投影數(shù)據(jù)恢復的低劑量腦灌注PCT成像方法。該方法充分考慮到腦PCT投影數(shù)據(jù)的統(tǒng)計分布特性,利用該特性進行建模,采用PWLS的方法進行數(shù)據(jù)恢復,利用高斯-賽德爾(Gauss-Seidel,GS)方式進行迭代求解。不僅如此,還在原始投影數(shù)據(jù)和PWLS恢復后的投影數(shù)據(jù)之間引入自適應加權處理,更好地恢復投影數(shù)據(jù)。本方法有以下幾大優(yōu)點:①充分利用投影數(shù)據(jù)的統(tǒng)計分布特性,對噪聲分布了解更為深入,有助于噪聲去除;②采用了 GS優(yōu)化算法,迭代求解;③根據(jù)投影數(shù)據(jù)噪聲的水平,引入自適應投影數(shù)據(jù)加權處理,使得重建結果更為準確。該方法恢復重建的結果較傳統(tǒng)濾波形式的結果噪聲程度更小。(2)提出一種基于雙字典學習(couple dictionary learning,CDL)的低劑量動態(tài)腦灌注成像方法。該方法將PCT序列數(shù)據(jù)中的背景信息和增強信息分離開來,采用 K-SVD(K-singular value decomposition,K-SVD)字典學習的方式分別訓練一個二維背景信息字典和三維增強信息字典,用以恢復低劑量PCT序列圖像,進一步獲取高質量的腦血流灌注參數(shù)圖。本方法有以下大幾優(yōu)點:①充分考慮PCT序列圖像的結構特點,將背景信息和增強信息區(qū)分處理;②考慮到低劑量PCT序列圖像的噪聲程度,在三維增強字典的訓練中引入了常規(guī)劑量的PCT序列作為外援先驗信息。由于適當?shù)匾胂闰炐畔⑴c時間信息,恢復的PCT序列較其他幾種基于字典學習的結果更優(yōu)。
[Abstract]:Acute ischemic stroke (acute ischemic stroke, AIS), also known as acute cerebral infarction, is the most common cerebrovascular disease, which is the most important cause of death and disability in the elderly in our country. Cerebral perfusion CT (perfusion CT, PCT) is the clinical cause. It is widely used in the examination of AIS patients, but continuous dynamic scanning of the lesion makes the patient bear a great risk of dose radiation and even induce some genetic diseases. How to reduce the dose of the scan without affecting the diagnostic image quality is an urgent problem in the current CT field. At present, the way to reduce the dose of CT radiation is in a way. Many, it is a simple and economical method to minimize mAs in the condition of meeting the needs of clinical diagnosis. However, the number of photons collected by the detector is reduced dramatically, and the projection data is polluted by a large number of photon noise. The quality of the reconstructed image is seriously damaged, and the image is shown to be a large number of noises in the image and a large amount of noise in the image. For this low mAs technology, in order to obtain the final high quality parameters, we can start from the projection data to restore the string map data contaminated by the noise, or to reconstruct the high quality PCT sequence images using the alternative method of iterative reconstruction, and can also start from the PCT sequence image itself, and design all kinds of filters for post processing to restore the image. The image of high quality PCT sequence is complex, and the suitable regularization can be used in the quantitative parameter of deconvolution estimation, and the blood perfusion parameters can be obtained directly from the clinical diagnosis. According to the statistical analysis of a large number of experimental data, the CT projection data have some statistical characteristics. On the other hand, in the preliminary reconstructed low dose PCT sequence images, we can observe that the sequence images have the same background information. This part of the fixed anatomical structure remains basically the same in the process of the contrast of the contrast agent in the tissue, so we can divide the PCT sequence image into an image. The background and the enhanced part are considered. Observing the pixel point CT of an tissue perfusion information is worth changing (enhancement). It is not only related to the CT value of a single pixel point, but also a regional effect rather than a single pixel. It is more practical to treat the image as a lot of blocks. The following are as follows: (1) a low dose cerebral perfusion PCT imaging method based on the penalized weighted least-square (PWLS) projection data recovery is proposed. This method takes full account of the statistical distribution characteristics of the brain PCT projection data, uses this characteristic to model, uses the PWLS method to restore the data, and uses Gauss Sede. The method of Gauss-Seidel (GS) is iteratively solved. Not only that, it also introduces adaptive weighted processing between the original projection data and the projected data after the PWLS recovery to better restore the projection data. This method has the following several advantages: (1) making full use of the statistical distribution characteristics of the projected data to understand the noise distribution more deeply and helps to help it. Noise removal; (2) using the GS optimization algorithm and iterative solution; thirdly, based on the level of the projection data noise, the adaptive projection data weighting is introduced to make the reconstruction results more accurate. The result of the reconstruction is smaller than the result of the traditional filtering. (2) a new type of dual dictionary learning (couple dictionary LEA) is proposed. Rning, CDL) the low dose dynamic brain perfusion imaging method. This method separates the background information from the PCT sequence data and the enhancement information, and uses the K-SVD (K-singular value decomposition, K-SVD) dictionary to train a two-dimensional background information dictionary and the three dimension enhancement information dictionary to restore the low dose PCT sequence diagram. For example, further obtaining high quality cerebral blood flow perfusion parameters. This method has the following advantages: (1) taking full account of the structural features of PCT sequence images and distinguishing the background information from the enhanced information; secondly, taking into account the noise level of the low dose PCT sequence image, the conventional dose PCT sequence is introduced in the training of the three-dimensional enhancement dictionary. Due to the introduction of prior information and time information, the restored PCT sequence is better than other dictionary based learning results.

【學位授予單位】：南方醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R743.3;R816.1

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