本文選題：中樞神經系統 + 擴散張量成像　； 參考：《華中科技大學》2013年博士論文

【摘要】：第一部分APP/PS1轉基因小鼠活體全腦DTI定量研究 目的：以往的研究已將擴散張量成像(diffusion tensor imaging, DTI)應用于阿爾茨海默病(Alzheimer's disease, AD)動物模型的組織病理學研究中,但是少有關于結構特異性方面的報道�；隗w素的分析方法(voxel-based analysis, VBA)和基于解剖圖譜的分析方法(atlas-based analysis, ABA)是DTI全腦分析方法中兩種互補的方法。本研究的目的在于采用全腦DTI的分析方法,明確AD動物模型病理變化的空間位置分布特征。 材料與方法：同時采用VBA和ABA的方法,對APP/PS1轉基因小鼠(n=9)和野生型對照(n=9)進行全腦的DTI對比分析。采用多種度量指標,如各向異性分數(fractional anisotropy, FA)、擴散軌跡(total diffusivity, trace)、軸向彌散(axial diffusivity, DA)和放射彌散(radial diffusivity, DR)對阿爾茨海默病小鼠不同類型的病理變化進行量化分析。采用Kappa分析的方法對手動描繪的感興趣區(qū)(region of interest, ROI)和基于解剖圖譜方法所勾畫的ROI進行比較,以評估圖像配準的準確性。MR檢查之后,對APP/PS1轉基因小鼠和野生型對照進行組織學檢查分析。 結果：結果顯示,APP/PS1轉基因小鼠存在廣泛的腦結構異常,包括灰質區(qū)域如新皮層、海馬、紋狀體、丘腦、下丘腦、屏狀核、杏仁核及梨狀皮層,和白質區(qū)域如胼胝體/外囊、扣帶束、隔、內囊、海馬傘及視束,均表現為FA值或DA值升高,或者FA值和DA值同時升高(p0.05,FDR校正)。手動描繪的ROI與ABA方法所描繪的ROI之間的平均Kappa值均接近0.8,且在APP/PS1轉基因小鼠組和野生型對照組之間無顯著性差異(p0.05)。組織病理學分析證實了灰質區(qū)域如新皮層和海馬區(qū)微結構的DTI變化。DTI同時也發(fā)現了廣泛的白質區(qū)域的彌散改變,但這種差異僅靠單層的組織學定性觀察難以準確評估。 結論：本研究報道了APP/PS1轉基因小鼠腦結構特異性的病理變化,同時也證實了全腦DTI定量分析方法在AD動物模型中的可行性。 第二部分AD、MCI和健康人群腦白質差異的空間分布模式探討 目的：近年來大量研究均發(fā)現阿爾茨海默病(AD)患者、輕度認知障礙(MCI)患者和健康人群的腦白質完整性存在顯著差異,然而AD和MCI患者腦白質損害的空間分布模式少有報道。本研究旨在通過全腦的DTI定量分析,探討AD、MCI和健康人群腦白質差異的空間分布模式,找到疾病早期診斷和監(jiān)測疾病進展的可靠指標。 材料與方法：依據NINCDS-ADRDA可能AD的標準納入AD患者21例(M/F=11/10,平均年齡66.8歲)；依據Petersen的標準納入MCI患者8例(M/F=3/5,平均年齡64.4歲)；及無神經系統疾病的健康對照15例(M/F=8/7,平均年齡65.3歲)。采用GE公司signa HDxt3.0Tesla超導磁共振掃描儀行擴散張量成像(diffusion tensor imaging,DTI),掃描參數如下：TR/TE=10000/83ms, FA=90°, Matrix=256x256, FOV=240mmx240mm, Phase FOV=1,層厚3.0mm無間隔,NEX=1,42層覆蓋全腦,b值為1000s/mm2,30個方向。得到DTI原始圖像之后,利用DTIstudio軟件進行FA圖重建,利用DiffeoMap軟件對圖像進行基于解剖圖譜的分析,測量深部灰質和深部白質共58個腦區(qū)結構的FA值。AD、MCI和健康對照組58個腦區(qū)結構的FA值首先采用單因素方差分析并進行事后檢驗,兩兩比較組間差異；然后對相關腦區(qū)FA值與簡易精神狀態(tài)量表(mini-mental state examination, MMSE)評分做相關分析。 結果：與健康人群相比,AD患者深部灰質和深部白質結構存在廣泛的FA值降低(p0.05,FDR校正)。其中,胼胝體壓部和丘腦的FA值在MCI組和健康對照組間存在顯著差異(p0.05,FDR校正),但在AD組和MCI組間無差異(p0.05)；扣帶束和上縱束等8個結構的FA值在AD組和MCI組間有顯著差異(p0.05,FDR校正),但在MCI組和健康對照組間無差異(p0.05)。相關分析顯示,扣帶束和上縱束的FA值與MMSE評分存在顯著的正相關關系,以右側扣帶束的相關系數值最高(r=0.606,p=0.001)；而胼胝體壓部和丘腦區(qū)域FA值與MMSE不存在相關關系(p0.05)。 結論：AD和MCI患者腦白質損害的空間分布模式存在顯著差異。胼胝體壓部和丘腦顯微結構病變是早期事件,與認知功能下降關系不大。而扣帶束和上縱束白質病變與疾病進展有關,與認知功能下降顯著相關。 第三部分定量結構MRI對阿爾茨海默病的鑒別診斷研究 目的：提出一種全新的方法,可將腦部T1加權磁共振(magnetic resonance, MR)圖像轉變?yōu)樘卣魇噶?應用于基于內容的圖像檢索(content-based image retrieval, CBIR)。為了克服臨床中同一人群的解剖學個體差異及成像參數的不一致性,我們提出了一種基于目標圖像與解剖圖譜之間差異的圖像分析方法(Gap between an Atlas and a target Image Analysis, GAIA),利用基于解剖圖譜的圖像分割方法(atlas-based analysis, ABA),尋找目標圖像與解剖圖譜之間差異的大小,從中提取目標圖像的解剖學特征,用于阿爾茨海默病的鑒別診斷研究。 材料與方法：選取阿爾茨海默病(Alzheimer's disease, AD)、亨廷頓病(Huntington's disease, HD)、脊髓小腦性共濟失調6型(Spinocerebral ataxia type6, SCA6)、原發(fā)性進行性失語癥(primary progressive aphasia, PPA)患者及正常人的T1加權MR圖像共102例,作為訓練數據。另外隨機選取AD、HD、SCA6、PPA患者及正常人的T1加權MR圖像共170例作為測試數據。采用GAIA的方法對訓練數據進行模式分類,分別提取AD、HD、SCA6、PPA患者及正常人的神經解剖學特征作為特征矢量；隨后將這些特征矢量應用到測試數據中,每一個測試數據分別得到一個判別得分(discriminant score),利用判別得分對其進行病種的判別,并評估GAIA判別不同種類疾病的準確性。 結果：從訓練數據中提取出來的特征矢量,與我們所選取的各神經變性疾病所對應的病理學標志完全一致。大部分測試數據的判別得分能夠準確的將其分類至各自對應的疾病種類中去。不具備該疾病典型相關解剖學特征的數據不能被準確分類。GAIA可將阿爾茨海默病從其它類型的神經變性疾病中區(qū)分開來。 結論：我們提出的GAIA方法,是基于疾病相關的解剖學特征的提取方法,在圖像的特征提取與模式識別中有著廣闊的應用前景。在未來,可使得放射科醫(yī)生只需要提交一名患者的圖像,就能夠將具有類似解剖學特征的相關臨床病例全部檢索出來,從而對某種疾病的診斷、治療、預后及隨訪預測進行大樣本的人口學普查及統計分析。
[Abstract]:Part one quantitative study of whole brain DTI in APP/PS1 transgenic mice
Objective: Previous studies have applied diffusion tensor imaging (DTI) to the histopathological study of the animal model of Alzheimer's disease (AD), but there are few reports on structural specificity. The voxel based analysis (voxel-based analysis, VBA) and anatomic map based Atlas-based analysis (ABA) is the two complementary method in the DTI whole brain analysis. The purpose of this study is to identify the spatial distribution characteristics of the pathological changes in the AD animal model by using the whole brain DTI analysis method.
Materials and methods: at the same time, VBA and ABA were used to compare the whole brain DTI of APP/PS1 transgenic mice (n=9) and wild type control (n=9). A variety of metrics, such as the anisotropy fraction (fractional anisotropy, FA), the diffusion trajectory (total diffusivity, trace), axial dispersion, and radiation diffusion were used. Dial diffusivity, DR) quantified the pathological changes of different types of Alzheimer's disease mice. Kappa analysis was used to compare the manually depicted region of interest (region of interest, ROI) and ROI based on the anatomic mapping method to evaluate the accuracy of the image registration by.MR, and to APP/PS1 GM The mice and wild type control were examined histologically.
Results: the results showed that the APP/PS1 transgenic mice had extensive brain structural abnormalities, including the gray matter regions such as the new cortex, the hippocampus, the striatum, the thalamus, the hypothalamus, the screen nucleus, the amygdala and the pyriform cortex, and the white matter areas such as the corpus callosum / outer capsule, the buckle band, the septum, the internal capsule, the hippocampal umbrella and the optic tract, or the value of the FA and the DA, or the value of the FA and DA. The average value of the value increased simultaneously (P0.05, FDR correction). The average Kappa value between the manual depicted ROI and the ABA method was close to 0.8, and there was no significant difference between the APP/PS1 transgenic mice and the wild type control group (P0.05). The histopathological analysis confirmed that the DTI change.DTI of the gray matter region, such as the neocortex and the hippocampus microstructures, was also at the same time Extensive changes in the white matter area were found, but the difference was difficult to accurately assess by single layer histological observation.
Conclusion: This study reported the pathological changes in the specific brain structure of APP/PS1 transgenic mice, and also confirmed the feasibility of DTI quantitative analysis in the AD animal model.
The second part is the spatial distribution pattern of white matter difference between AD, MCI and healthy people.
Objective: in recent years, a large number of studies have found significant differences in white matter integrity between patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy people. However, there are few reports of spatial distribution patterns of brain white matter damage in AD and MCI patients. This study aims to explore AD, MCI and healthy brain by quantitative analysis of DTI in the whole brain. The spatial distribution pattern of white matter is a reliable index for early diagnosis and monitoring of disease progression.
Materials and methods: 21 cases of AD patients (M/F=11/10, mean age 66.8 years) were included according to the standard of NINCDS-ADRDA possible AD; 8 cases of MCI patients (M/F=3/5, average age 64.4 years old) were incorporated according to the Petersen standard; 15 healthy controls (M/F=8/7, mean age 65.3) with no nervous system disease (M/F=8/7, 65.3 years old). Diffusion tensor imaging (DTI), the scanning parameters are as follows: TR/TE=10000/83ms, FA=90, Matrix=256x256, FOV=240mmx240mm, Phase FOV=1, layer thickness 3.0mm spacer, NEX=1,42 layer covering the whole brain. The DiffeoMap software was used to analyze the image based on the anatomic map, to measure the FA value.AD of 58 brain regions in deep gray matter and deep white matter. The FA values of 58 brain regions in the MCI and the healthy control group were first used for single factor analysis of variance and after the post test, and 22 of the differences were compared. Then the FA value and simple spirit in the related brain regions were compared. Mini-Mental State Examination (MMSE) score was used for correlation analysis.
Results: compared with the healthy population, the deep gray matter and deep white matter structure of the AD patients had extensive FA values (P0.05, FDR correction). There were significant differences between the corpus callosum pressure and the FA value of the thalamus between the MCI group and the healthy control group (P0.05, FDR correction), but there was no difference between the AD group and the MCI group (P0.05); the 8 structures of the cingulate bundle and the upper longitudinal bundle were found. The FA values were significantly different between the AD group and the MCI group (P0.05, FDR correction), but there was no difference between the MCI group and the healthy control group (P0.05). The correlation analysis showed that there was a significant positive correlation between the FA value of the cingulate bundle and the upper longitudinal beam and the MMSE score, which was the highest (r=0.606, p=0.001) of the right cingulate band (r=0.606, p=0.001), and the corpus callosum pressure and the thalamus region F. There is no correlation between the A value and the MMSE (P0.05).
Conclusion: there are significant differences in spatial distribution patterns of brain white matter damage in AD and MCI patients. The lesions of the corpus callosum and thalamus are early events and have little to do with the decline of cognitive function. The buckle and upper longitudinal bundle white matter is related to the disease progression, which is significantly related to the decline of cognitive function.
The third part is quantitative structure MRI in the differential diagnosis of Alzheimer's disease.
Objective: to propose a new method to transform the T1 weighted magnetic resonance (MR) image into the feature vector and apply it to the content based image retrieval (content-based image retrieval, CBIR). In order to overcome the inconsistency of the individual differences and the imaging parameters of the same population in the clinic, we put forward a new method. Gap between an Atlas and a target Image Analysis, GAIA, based on the difference between the target image and the anatomical map, and using the image segmentation method based on the anatomic map (atlas-based analysis) to find the difference between the target image and the anatomic map, and extract the anatomical features of the target image from the image analysis method. A study on the differential diagnosis of Alzheimer's disease.
Materials and methods: Alzheimer's disease (AD), Huntington's disease (Huntington's disease, HD), spinal cerebellar ataxia type 6 (Spinocerebral ataxia type6, SCA6), and 102 cases of primary progressive aphasia (primary progressive) and normal people were used as training data. In addition, a total of 170 cases of T1 weighted MR images of AD, HD, SCA6, PPA and normal people were selected as test data. The training data were classified by GAIA method, and the neuroanatomical features of AD, HD, SCA6, PPA patients and normal people were extracted respectively as feature vectors, and then these feature vectors were applied to the test data, each of which was applied to each of the test data, each of which was applied to the test data, each of which was applied to each of the test data. A discriminant score (discriminant score) was obtained for the test data, and the discriminant score was used to discriminate the disease and evaluate the accuracy of GAIA to distinguish the different kinds of diseases.
Results: the feature vectors extracted from the training data are in complete agreement with the pathological signs corresponding to the neurodegenerative diseases we selected. Most of the test data can be accurately classified into their respective disease types. Data that does not possess the typical anatomical characteristics of the disease can not be found. Accurate classification of.GAIA can distinguish Alzheimer's disease from other types of neurodegenerative diseases.
Conclusion: the GAIA method, which we propose, is based on the extraction of disease-related anatomical features, and has a broad application prospect in image feature extraction and pattern recognition. In the future, the radiologist can only submit one patient's image to all related clinical cases with similar anatomical characteristics. A large population census and statistical analysis of the diagnosis, treatment, prognosis and follow-up prediction of a disease are conducted.
【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：R445.2;R749.16

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