本文選題：磁共振彌散加權成像 + 纖維示蹤�。� 參考：《太原理工大學》2017年碩士論文

【摘要】：隨著活體磁共振彌散加權成像宏觀連接組映射的快速發(fā)展,當務之急是根據這些重構數據得到連接強度的衡量指標,并從網絡自身角度驗證這些連接的可靠性。在猴腦的宏觀連接領域,重構纖維束數量更多的被用來作為皮層區(qū)域間連接強度的指標,并且通過統(tǒng)計分析驗證基于DWI的纖維束數量與動物活體的示蹤解剖連接強度之間存在正相關的關系。近年來,磁共振彌散加權成像得到了廣泛的應用,但是仍然有一些問題尚未得到解決,比如,獲取到的影像數據存在容積效應、纖維交叉的問題。在纖維追蹤方法方面,也存在一定的問題,比如參數設置的問題等。由于掃描技術以及追蹤技術的問題使得根據纖維方法構造得到的腦網絡中存在一定數量的假陽性連接以及假陰性連接;還有,對于長距離連接,通過現(xiàn)有的技術總是很難捕捉到的,而這些問題會導致網絡結構有很大的變化,使得依據該網絡結構所得到的結論失去真實性。對于非人類的哺乳動物來說,可以得到真實網絡,來對比構造網絡的正確性,若無法獲取得到真實的網絡結構(比如人腦),則構造得到的網絡中的連接是否可靠以及可靠程度是多少,這方面的研究是比較少的。在本文中,對DWI的派生指標與示蹤灌注強度的指標進行了比較,并使用隨機分塊模型對DWI派生出的連接進行了評價,對評價結果與灌注強度的指標,以及dwi得到連接的其他衡量指標進行了比較。區(qū)域連接的連接強度的示蹤信息是從兩個通用的猴連接組數據集中獲得,包括(1)cocomac數據庫,收集了猴腦的示蹤實驗數據,(2)高分辨率的示蹤數據集,是由markov和kennedy以及他們的合作伙伴提供的。網絡中的數據表示的是重構的纖維路徑的纖維束數量,是從23只猴子獲得的dwi數據所得到的,對得到的23個個體網絡使用符號檢驗得到中樞網絡,從而,對中樞網絡中的連接進行評價,結果發(fā)現(xiàn),連接的可靠性值與連接強度的指標是正相關的(p值都小于0.05),與基于dwi得到的連接的其他衡量指標間同樣存在強烈相關。在該結論的基礎上,將解決重構網絡中存在的假陽性連接與假陰性連接的問題,優(yōu)化網絡的結構。本文的主要創(chuàng)新工作有以下幾點:第一,基于隨機分塊模型網絡連接可信度計算。按照隨機分塊模型的思想,將網絡中的節(jié)點隨機劃分為相同或不同的組,節(jié)點之間的連接是否可靠以及可靠的程度主要依賴與節(jié)點所存在的組。通過將連接的可靠性值與連接的真實強度進行相關性分析,結果表明基于隨機分塊模型得到的連接可靠性與真實強度存在強烈的正相關。第二,結構腦網絡連接可靠性驗證。將計算所得到的可靠性的值與連接的其他指標進行相關性分析,指標包括纖維束數量,各向異性值,距離值,平均體素個數,結果表明,纖維束數量,各向異性值與平均體素數量這3個指標均與連接的可靠性存在強烈的正相關,距離值指標與可靠性存在強烈的負相關,即基于隨機分塊模型的方法可以適用于腦結構網絡的研究。第三,結構腦網絡連接優(yōu)化。使用成像技術得到的網絡中存在一定的假陽性連接與假陰性連接,在本文中使用連接可靠性的值以及符號檢驗的值對網絡中的這兩類連接進行優(yōu)化。結果表明,使用可靠性的值優(yōu)化后的網絡與真實網絡更接近,可靠性值可以用于腦結構網絡的優(yōu)化。本文提出基于隨機分塊模型對DWI的網絡連接進行評價的方法,可以正確的計算連接的可靠性,并以此來優(yōu)化網絡結構。實驗的結果顯示,基于隨機分塊模型的評價值對白質連接的灌注強度以及連接的其他指標提供近乎真實的估計。本文通過對兩種模式的交叉比較提出了一個觀點:基于隨機分塊模型的方法可以作為連接評價的有效方法論。
[Abstract]:With the rapid development of the macroscopical connection group mapping of DWI, the urgent task is to obtain the measurement index of connection strength based on these reconstructed data and verify the reliability of these connections from the network itself. The number of reconstructed fiber bundles is used more as the cortical interconnect in the macaque brain connection field. There is a positive correlation between the number of fiber bundles based on DWI and the intensity of the tracer anatomical connection of animal living body through statistical analysis. In recent years, magnetic resonance diffusion weighted imaging has been widely used, but there are still some problems that have not been solved. For example, the image data obtained are available. There are some problems in fiber tracking, such as the problem of parameter setting, etc., such as the problem of parameter setting, and so on. Due to the problems of scanning and tracking technology, there are a number of false positive connections and false negative connections in the brain network constructed according to the fiber method, and the long distance connection, It is difficult to capture by existing technology, and these problems can lead to a great change in the network structure, which makes the conclusion of the network structure lose authenticity. For non human mammals, the real network can be obtained to compare the correctness of the structure network, if the real network node can not be obtained. In this paper, the derivation index of DWI and the index of tracer perfusion intensity are compared, and a random partitioned model is used to evaluate the connections derived from DWI, and the results and perfusion of the evaluation are given in this paper. The strength index, as well as the other measurements that DWI gets connected, is compared. The tracing information for the connection strength of the region is obtained from two common monkey connection group data sets, including (1) CoCoMac database, collecting the monkey brain tracer experimental data, and (2) high resolution tracer data sets, which are Markov and Kennedy, and they The data in the network represent the number of fiber bundles of the reconstructed fiber path, which is obtained from the DWI data obtained by 23 monkeys. The central network is obtained by using symbolic tests for the obtained 23 individual networks, so that the connections in the central network are evaluated, and the results are found, and the connection reliability values and connections are found. The index of intensity is positively correlated (P value is less than 0.05), and there is a strong correlation with other metrics based on DWI based connections. On the basis of this conclusion, the problem of false positive connections and false negative connections in the reconfigurable network will be solved and the network structure is optimized. The main innovations of this paper are as follows: first, Based on the stochastic block model network connection reliability calculation. According to the idea of random block model, the nodes in the network are randomly divided into the same or different groups. The reliability and reliability of the connections between nodes depend mainly on the groups existing in the nodes. The results show that there is a strong positive correlation between the reliability and the true strength of the connection based on the random block model. Second, the reliability verification of the structural brain network connection. The correlation analysis of the calculated reliability values and the other indexes of the connection is carried out, including the number of fiber bundles, the anisotropic values, the distance values, and the average. The number of voxels shows that there is a strong positive correlation between the number of fiber bundles, the 3 indexes of the anisotropic value and the average volume of body element, and there is a strong negative correlation between the distance value index and the reliability. That is, the method based on the random block model can be applied to the study of the brain structure network. Third, the structure of the brain network is superior to the structural network. There are certain false positive connections and false negative connections in the network using imaging technology. In this paper, the values of connection reliability and the value of symbol test are used to optimize the two types of connections in the network. The results show that the network with the reliability value optimized is closer to the real network, and the reliability value can be used in the brain. In this paper, a method of evaluating the network connection of DWI based on random block model is proposed, which can correctly calculate the reliability of the connection and optimize the network structure. The experimental results show that the evaluation value based on the random partitioned model provides close proximity to the perfusion intensity of white connection and other indicators of connection. Real estimation. This paper puts forward a point of view through the cross comparison of two models: a method based on random block model can be used as an effective methodology for connection evaluation.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R338;O157.5

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相關期刊論文 前1條

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本文編號：1984285