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(zhuǎn)基因小鼠(n=9)和野生型對照(n=9)進(jìn)行全腦的DTI對比分析。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(jìn)行比較,以評估圖像配準(zhǔn)的準(zhǔn)確性。MR檢查之后,對APP/PS1轉(zhuǎn)基因小鼠和野生型對照進(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(zhuǎn)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(xué)分析證實了灰質(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)確評估。 結(jié)論：本研究報道了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(zhì)差異的空間分布模式探討 目的：近年來大量研究均發(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)；及無神經(jīng)系統(tǒng)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(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(jìn)行FA圖重建,利用DiffeoMap軟件對圖像進(jìn)行基于解剖圖譜的分析,測量深部灰質(zhì)和深部白質(zhì)共58個腦區(qū)結(jié)構(gòu)的FA值。AD、MCI和健康對照組58個腦區(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,兩兩比較組間差異；然后對相關(guān)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(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(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組和健康對照組間無差異(p0.05)。相關(guān)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(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例作為測試數(shù)據(jù)。采用GAIA的方法對訓(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)用到測試數(shù)據(jù)中,每一個測試數(shù)據(jù)分別得到一個判別得分(discriminant score),利用判別得分對其進(jìn)行病種的判別,并評估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)中提取出來的特征矢量,與我們所選取的各神經(jīng)變性疾病所對應(yīng)的病理學(xué)標(biāo)志完全一致。大部分測試數(shù)據(jù)的判別得分能夠準(zhǔn)確的將其分類至各自對應(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。 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別中有著廣闊的應(yīng)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?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,從而對某種疾病的診斷、治療、預(yù)后及隨訪預(yù)測進(jìn)行大樣本的人口學(xué)普查及統(tǒng)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(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：R445.2;R749.16

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 解恒革,王魯寧,程流泉,蔡幼輇;阿爾茨海默病和血管性癡呆患者海馬杏仁核體積的 MRI對比研究[J];現(xiàn)代康復(fù);2001年01期

2 K. Ishii;T. Kanda;A. Harada;N. Miyamoto;T. Kawaguchi;K. Shimada;李志明;;胼胝體的角度在診斷特發(fā)性正常壓力腦積水中的臨床影響[J];國際醫(yī)學(xué)放射學(xué)雜志;2009年01期

3 沈晶;M.C. Evans;J. Barnes;C. Nielsen;L.G. Kim;S.L. Clegg;M. Blair;;阿爾茨海默病和輕度認(rèn)知功能障礙中的腦體積改變:認(rèn)知相關(guān)研究[J];國際醫(yī)學(xué)放射學(xué)雜志;2010年03期

4 戴艷萍;馮濤;遇亞南;靳美;曹利;;血管性癡呆與阿爾茨海默病的臨床特征比較[J];黑龍江醫(yī)學(xué);2006年11期

5 婁昕,蔡幼銓;阿爾茨海默病早期影像學(xué)診斷新進(jìn)展[J];中國醫(yī)學(xué)影像學(xué)雜志;2004年04期

6 高明能,梁碧玲,黃穗喬,葉瑞心,鐘鏡聯(lián);脊髓拴系綜合征的MRI診斷評價[J];影像診斷與介入放射學(xué);1997年02期

7 史學(xué)芳,張慶俊,張更申,田劍光,王增智;椎管內(nèi)結(jié)核瘤的診斷與治療[J];中國臨床神經(jīng)外科雜志;1998年02期

8 趙薛旭,劉文,李作漢,狄晴,曹輝,陳月琴;血管性帕金森綜合征的臨床、血液流變學(xué)與MRI[J];中風(fēng)與神經(jīng)疾病雜志;1999年03期

9 陸建平,王莉,王飛,劉崎,田建明,王一,龔建國,金愛國,曾浩;肝膿腫的MRI分型和診斷[J];中國醫(yī)學(xué)計算機成像雜志;2000年03期

10 張宏權(quán),姜曉梅,楊曉娟;基底動脈尖綜合征2例報告[J];齊齊哈爾醫(yī)學(xué)院學(xué)報;2001年05期

相關(guān)會議論文 前10條

1 肖夢強;劉金豐;郭治平;李玉英;譚麗容;;跟腱損傷的MRI表現(xiàn)[A];第十一次全國中西醫(yī)結(jié)合影像學(xué)術(shù)研討會暨全國中西醫(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

2 翟昭華;任勇軍;徐文杰;陳浩林;;急性Co中毒中樞神經(jīng)系統(tǒng)MRI表現(xiàn)[A];第十一次全國中西醫(yī)結(jié)合影像學(xué)術(shù)研討會暨全國中西醫(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

3 陳玉琴;錢海馨;石慧敏;;MRI在鉭墊治療顳下頜關(guān)節(jié)盤移位效果評價中的作用[A];第八屆全國顳下頜關(guān)節(jié)病學(xué)及（牙合）學(xué)大會論文匯編[C];2011年

4 蔡慶;沈玉英;;乳腺癌動態(tài)增強MRI與X線攝像的對比分析[A];蘇州市自然科學(xué)優(yōu)秀學(xué)術(shù)論文匯編(2008-2009)[C];2010年

5 王淑麗;張曉林;張曉光;;動態(tài)MRI及斜矢狀MRI在頸椎病檢查中的應(yīng)用價值[A];中華醫(yī)學(xué)會第十八次全國放射學(xué)學(xué)術(shù)會議論文匯編[C];2011年

6 Orest Boyko;;MRI Gadolinium contrast use:Efficacy and Safety[A];中華醫(yī)學(xué)會第十八次全國放射學(xué)學(xué)術(shù)會議論文匯編[C];2011年

7 王紹武;王玉麗;;軟組織血管瘤與神經(jīng)鞘腫瘤的MRI鑒別診斷[A];中華醫(yī)學(xué)會第十八次全國放射學(xué)學(xué)術(shù)會議論文匯編[C];2011年

8 郭治平;劉金豐;肖夢強;;關(guān)節(jié)軟骨MRI進(jìn)展[A];第十一次全國中西醫(yī)結(jié)合影像學(xué)術(shù)研討會暨全國中西醫(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)會影像技術(shù)分會第十八次全國學(xué)術(shù)大會論文集[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)會影像技術(shù)分會第十八次全國學(xué)術(shù)大會論文集[C];2010年

相關(guān)重要報紙文章 前10條

1 阿勝　編譯;打開AD新的探索之門[N];醫(yī)藥經(jīng)濟(jì)報;2009年

2 王延江;阿爾茨海默病 影像診斷的飛躍[N];健康報;2011年

3 李勇;阿爾茨海默病防治藥物開發(fā)方向漸明[N];中國醫(yī)藥報;2011年

4 記者　錢錚;海鞘含縮醛磷脂能治阿爾茨海默病[N];新華每日電訊;2006年

5 ;世界阿爾茨海默病日主題：“行動改變未來”[N];醫(yī)藥經(jīng)濟(jì)報;2005年

6 余志平;聚焦阿爾茨海默病診療研究[N];中國醫(yī)藥報;2004年

7 李文;阿爾茨海默病研究不斷出新[N];中國醫(yī)藥報;2004年

8 王迪;生命競賽[N];醫(yī)藥經(jīng)濟(jì)報;2007年

9 編譯 伊遙;從失敗中汲取教訓(xùn) 從進(jìn)步中探尋希望[N];中國醫(yī)藥報;2011年

10 聞新;早期診斷阿爾茨海默病新依據(jù)被發(fā)現(xiàn)[N];醫(yī)藥經(jīng)濟(jì)報;2006年

相關(guān)博士學(xué)位論文 前10條

1 覃媛媛;全腦定量結(jié)構(gòu)MRI和DTI對阿爾茨海默病的實驗和臨床研究[D];華中科技大學(xué);2013年

2 潘文宇;一種新型MRI譜儀的設(shè)計及關(guān)鍵技術(shù)研究[D];中國科學(xué)技術(shù)大學(xué);2011年

3 張忠和;大腦皮質(zhì)生前發(fā)育的高場強MRI研究[D];山東大學(xué);2011年

4 丁爽;MRI影像生物標(biāo)記物評價結(jié)腸癌裸鼠皮下移植瘤抗腫瘤血管生成藥物療效的實驗研究[D];新疆醫(yī)科大學(xué);2013年

5 王榮品;3T MRI在復(fù)雜型先天性心臟病雙向Glenn分流術(shù)后的臨床應(yīng)用研究[D];南方醫(yī)科大學(xué);2010年

6 樊令仲;基于MRI及薄層斷面解剖數(shù)據(jù)的人類小腦結(jié)構(gòu)分析[D];山東大學(xué);2010年

7 寧瑞鵬;用于引導(dǎo)HIFU治療的永磁開放式MRI系統(tǒng)研究[D];華東師范大學(xué);2011年

8 趙長安;鋁致阿爾茨海默病大鼠的效應(yīng)及補腎填精方的防治機制[D];河北醫(yī)科大學(xué);2002年

9 黃艷紅;植物雌激素對絕經(jīng)后阿爾茨海默病神經(jīng)保護(hù)機制的研究[D];第四軍醫(yī)大學(xué);2004年

10 顧建欽;老年期癡呆的中西醫(yī)結(jié)合施治及康復(fù)研究[D];華中科技大學(xué);2004年

相關(guān)碩士學(xué)位論文 前10條

1 李思瑤;MRI在阿爾茨海默病診斷中的應(yīng)用[D];復(fù)旦大學(xué);2010年

2 翟寧;靜脈血氧依賴MRI成像在體腦鐵含量評價研究[D];中南大學(xué);2010年

3 丁永梅;RGD-USPIO納米粒構(gòu)建及其在腫瘤MRI診斷中應(yīng)用[D];蘇州大學(xué);2010年

4 李毅;MRI輔助定位下舌鱗癌的分子邊界研究[D];中南大學(xué);2010年

5 溫悅;超聲、鉬靶及MRI對乳腺疾病診斷的對比性研究[D];河北醫(yī)科大學(xué);2010年

6 逄鑫;乳腺癌淋巴管生成及Podoplanin表達(dá)MRI評價的實驗研究[D];第三軍醫(yī)大學(xué);2010年

7 耿鶴群;胎兒基底核區(qū)發(fā)育的高場強MRI研究[D];山東大學(xué);2010年

8 尚會娟;膝關(guān)節(jié)慢性軟骨損傷的MRI診斷與關(guān)節(jié)鏡檢查結(jié)果的對比研究[D];吉林大學(xué);2010年

9 李敏;動態(tài)增強MRI、DWI、SWI對乳腺病變的鑒別價值及各MR參數(shù)與MVD、VEGF的相關(guān)性研究[D];中國人民解放軍軍事醫(yī)學(xué)科學(xué)院;2010年

10 楊禹;頸部淋巴結(jié)轉(zhuǎn)移性鱗癌與反應(yīng)增生性淋巴結(jié)炎的MRI研究[D];暨南大學(xué);2011年

，

本文編號：1976309