本文選題：中樞神經(jīng)系統(tǒng) + 擴(kuò)散張量成像�。� 參考：《華中科技大學(xué)》2013年博士論文

【摘要】：第一部分APP/PS1轉(zhuǎn)基因小鼠活體全腦DTI定量研究 目的：以往的研究已將擴(kuò)散張量成像(diffusion tensor imaging, DTI)應(yīng)用于阿爾茨海默病(Alzheimer's disease, AD)動(dòng)物模型的組織病理學(xué)研究中,但是少有關(guān)于結(jié)構(gòu)特異性方面的報(bào)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(bǔ)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(dòng)物模型病理變化的空間位置分布特征。 材料與方法：同時(shí)采用VBA和ABA的方法,對(duì)APP/PS1轉(zhuǎn)基因小鼠(n=9)和野生型對(duì)照(n=9)進(jìn)行全腦的DTI對(duì)比分析。采用多種度量指標(biāo),如各向異性分?jǐn)?shù)(fractional anisotropy, FA)、擴(kuò)散軌跡(total diffusivity, trace)、軸向彌散(axial diffusivity, DA)和放射彌散(radial diffusivity, DR)對(duì)阿爾茨海默病小鼠不同類型的病理變化進(jìn)行量化分析。采用Kappa分析的方法對(duì)手動(dòng)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(jìn)行比較,以評(píng)估圖像配準(zhǔn)的準(zhǔn)確性。MR檢查之后,對(duì)APP/PS1轉(zhuǎn)基因小鼠和野生型對(duì)照進(jìn)行組織學(xué)檢查分析。 結(jié)果：結(jié)果顯示,APP/PS1轉(zhuǎn)基因小鼠存在廣泛的腦結(jié)構(gòu)異常,包括灰質(zhì)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(zhì)區(qū)域如胼胝體/外囊、扣帶束、隔、內(nèi)囊、海馬傘及視束,均表現(xiàn)為FA值或DA值升高,或者FA值和DA值同時(shí)升高(p0.05,FDR校正)。手動(dòng)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(zhuǎn)基因小鼠組和野生型對(duì)照組之間無(wú)顯著性差異(p0.05)。組織病理學(xué)分析證實(shí)了灰質(zhì)區(qū)域如新皮層和海馬區(qū)微結(jié)構(gòu)的DTI變化。DTI同時(shí)也發(fā)現(xiàn)了廣泛的白質(zhì)區(qū)域的彌散改變,但這種差異僅靠單層的組織學(xué)定性觀察難以準(zhǔn)確評(píng)估。 結(jié)論：本研究報(bào)道了APP/PS1轉(zhuǎn)基因小鼠腦結(jié)構(gòu)特異性的病理變化,同時(shí)也證實(shí)了全腦DTI定量分析方法在AD動(dòng)物模型中的可行性。 第二部分AD、MCI和健康人群腦白質(zhì)差異的空間分布模式探討 目的：近年來(lái)大量研究均發(fā)現(xiàn)阿爾茨海默病(AD)患者、輕度認(rèn)知障礙(MCI)患者和健康人群的腦白質(zhì)完整性存在顯著差異,然而AD和MCI患者腦白質(zhì)損害的空間分布模式少有報(bào)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(zhì)差異的空間分布模式,找到疾病早期診斷和監(jiān)測(cè)疾病進(jìn)展的可靠指標(biāo)。 材料與方法：依據(jù)NINCDS-ADRDA可能AD的標(biāo)準(zhǔn)納入AD患者21例(M/F=11/10,平均年齡66.8歲)；依據(jù)Petersen的標(biāo)準(zhǔn)納入MCI患者8例(M/F=3/5,平均年齡64.4歲)；及無(wú)神經(jīng)系統(tǒng)疾病的健康對(duì)照15例(M/F=8/7,平均年齡65.3歲)。采用GE公司signa HDxt3.0Tesla超導(dǎo)磁共振掃描儀行擴(kuò)散張量成像(diffusion tensor imaging,DTI),掃描參數(shù)如下：TR/TE=10000/83ms, FA=90°, Matrix=256x256, FOV=240mmx240mm, Phase FOV=1,層厚3.0mm無(wú)間隔,NEX=1,42層覆蓋全腦,b值為1000s/mm2,30個(gè)方向。得到DTI原始圖像之后,利用DTIstudio軟件進(jìn)行FA圖重建,利用DiffeoMap軟件對(duì)圖像進(jìn)行基于解剖圖譜的分析,測(cè)量深部灰質(zhì)和深部白質(zhì)共58個(gè)腦區(qū)結(jié)構(gòu)的FA值。AD、MCI和健康對(duì)照組58個(gè)腦區(qū)結(jié)構(gòu)的FA值首先采用單因素方差分析并進(jìn)行事后檢驗(yàn),兩兩比較組間差異；然后對(duì)相關(guān)腦區(qū)FA值與簡(jiǎn)易精神狀態(tài)量表(mini-mental state examination, MMSE)評(píng)分做相關(guān)分析。 結(jié)果：與健康人群相比,AD患者深部灰質(zhì)和深部白質(zhì)結(jié)構(gòu)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(duì)照組間存在顯著差異(p0.05,FDR校正),但在AD組和MCI組間無(wú)差異(p0.05)；扣帶束和上縱束等8個(gè)結(jié)構(gòu)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(duì)照組間無(wú)差異(p0.05)。相關(guān)分析顯示,扣帶束和上縱束的FA值與MMSE評(píng)分存在顯著的正相關(guān)關(guān)系,以右側(cè)扣帶束的相關(guān)系數(shù)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(guān)關(guān)系(p0.05)。 結(jié)論：AD和MCI患者腦白質(zhì)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(jié)構(gòu)病變是早期事件,與認(rèn)知功能下降關(guān)系不大。而扣帶束和上縱束白質(zhì)病變與疾病進(jìn)展有關(guān),與認(rèn)知功能下降顯著相關(guān)。 第三部分定量結(jié)構(gòu)MRI對(duì)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(quán)磁共振(magnetic resonance, MR)圖像轉(zhuǎn)變?yōu)樘卣魇噶?應(yīng)用于基于內(nèi)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(xué)個(gè)體差異及成像參數(shù)的不一致性,我們提出了一種基于目標(biāo)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(biāo)圖像與解剖圖譜之間差異的大小,從中提取目標(biāo)圖像的解剖學(xué)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(jì)失調(diào)6型(Spinocerebral ataxia type6, SCA6)、原發(fā)性進(jìn)行性失語(yǔ)癥(primary progressive aphasia, PPA)患者及正常人的T1加權(quán)MR圖像共102例,作為訓(xùn)練數(shù)據(jù)。另外隨機(jī)選取AD、HD、SCA6、PPA患者及正常人的T1加權(quán)MR圖像共170例作為測(cè)試數(shù)據(jù)。采用GAIA的方法對(duì)訓(xùn)練數(shù)據(jù)進(jìn)行模式分類,分別提取AD、HD、SCA6、PPA患者及正常人的神經(jīng)解剖學(xué)特征作為特征矢量；隨后將這些特征矢量應(yīng)用到測(cè)試數(shù)據(jù)中,每一個(gè)測(cè)試數(shù)據(jù)分別得到一個(gè)判別得分(discriminant score),利用判別得分對(duì)其進(jìn)行病種的判別,并評(píng)估GAIA判別不同種類疾病的準(zhǔn)確性。 結(jié)果：從訓(xùn)練數(shù)據(jù)中提取出來(lái)的特征矢量,與我們所選取的各神經(jīng)變性疾病所對(duì)應(yīng)的病理學(xué)標(biāo)志完全一致。大部分測(cè)試數(shù)據(jù)的判別得分能夠準(zhǔn)確的將其分類至各自對(duì)應(yīng)的疾病種類中去。不具備該疾病典型相關(guān)解剖學(xué)特征的數(shù)據(jù)不能被準(zhǔn)確分類。GAIA可將阿爾茨海默病從其它類型的神經(jīng)變性疾病中區(qū)分開來(lái)。 結(jié)論：我們提出的GAIA方法,是基于疾病相關(guān)的解剖學(xué)特征的提取方法,在圖像的特征提取與模式識(shí)別中有著廣闊的應(yīng)用前景。在未來(lái),可使得放射科醫(yī)生只需要提交一名患者的圖像,就能夠?qū)⒕哂蓄愃平馄蕦W(xué)特征的相關(guān)臨床病例全部檢索出來(lái),從而對(duì)某種疾病的診斷、治療、預(yù)后及隨訪預(yù)測(cè)進(jìn)行大樣本的人口學(xué)普查及統(tǒng)計(jì)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(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：R445.2;R749.16

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8 郭治平;劉金豐;肖夢(mèng)強(qiáng);;關(guān)節(jié)軟骨MRI進(jìn)展[A];第十一次全國(guó)中西醫(yī)結(jié)合影像學(xué)術(shù)研討會(huì)暨全國(guó)中西醫(yī)結(jié)合影像學(xué)研究進(jìn)展學(xué)習(xí)班資料匯編[C];2010年

9 舒楠;;MRI對(duì)體質(zhì)過敏患者縱膈包塊放療定位的快速掃描序列[A];2010中華醫(yī)學(xué)會(huì)影像技術(shù)分會(huì)第十八次全國(guó)學(xué)術(shù)大會(huì)論文集[C];2010年

10 黃開平;;如何提高小兒MRI檢查成功率?[A];2010中華醫(yī)學(xué)會(huì)影像技術(shù)分會(huì)第十八次全國(guó)學(xué)術(shù)大會(huì)論文集[C];2010年

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