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研究生:柯盈君
研究生(外文):Ying-chun Ke
論文名稱:阿茲海默症之認知與腦影像診斷
論文名稱(外文):Cognition and brain image diagnosis of Alzheimer’s disease
指導教授:鄭為民鄭為民引用關係
指導教授(外文):Wei-min Jeng
學位類別:碩士
校院名稱:東吳大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:43
中文關鍵詞:神經纖維追蹤擴散張量影像非等向性指標
外文關鍵詞:TractographyDiffusion Tensor MRIAnisotropy Fractional
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阿茲海默病患臨床上發現其認知功能之異常,近年來診斷之依據常使用大腦擴散張量影像之神經纖維追蹤,目前在神經纖維追蹤(Tractography)演算法之發展大多以大腦內白質神經纖維束為主。在擴散張量影像(Diffusion Tensor MRI, DTI) 提供足夠訊號雜訊比,也就是不考慮雜訊所帶來影響前提之下,首先利用非等向性指標(Anisotropy Fractional, FA),將白質(White matter)與灰質(Gray matter)大致區別出來,以利於顯微結構追蹤演算法於尋找白質纖維束走向[11],並且日本學者比較了阿茲海默症病患與正常人腦部FA值發現患有阿茲海默症者在大腦後半部的白質均比正常人退化.而另一個常被利用之指標,也就是內積(Inner Product),當作判斷纖維向場均勻與否之重要條件。在擴散張量影像實際上的運算方法上,是藉由所計算出的特徵值與特徵向量分別代表擴橢圓球體的三個主軸方向以及所對應之擴散程度的大小,並且定義最大特徵值所對應的特徵向量為第一特徵向量方向(1st eigenvector),可用於表示每個像素的神經纖維走向,再進一步進行整腦的神經纖維追蹤[13]。由於在目前眾多的神經纖維追蹤演算法中,多半是在Matlab環境之下進行運算,但受Matlab記憶體上的限制往往會導致無法做全腦的分析,並且也有部分的演算法為了有效降低繁複計算所帶來的冗長計算時程,而採用近似值求解演算法,將會導致分析精確度的下降。因此,我們希望藉由不同神經纖維追蹤演算法的整合,有效改善處理單一演算法分析上的限制與分析結果的精確度。
The analysis of white matter fibers has been the attention for the development of tractography recently. Under the assumption that diffusion tensor images can provide enough signal-to-noise ratio, anisotropy fractional index is employed to separate the white matter and gray matter disregarding the noise impacts in order to tracking the white matter fibers. Inner-products is another frequently used index for judging the fiber uniformity of fiber directions. The computational method used for diffusion tensor images is based on the eigen values and eigen vectors representing the magnitudes of diffusion and principle axis directions respectively. The largest eigen value and its corresponding eigen vector will be used as the fiber direction for each image pixel and the whole brain fiber tracking follows. Due to the fact that the accuracy fiber tracking is accompanied by the complex computations, parallel methods are proposed to improve the performance and reduce the time required.
誌謝..........................................................................................i
中文摘要..........................................................................................ii
英文摘要……………………..............................................................................iii
目錄...............................................................................................iv
圖目錄...................................................................vi
表目錄............................................................................viii
1. 緒論............................................................................1
1.1 研究動機.....................................................................1
1.2 研究目的......................................................................2
2. 文獻探討.............................................................................4
2.1 認知與腦部影像技術..........................................................4
2.1.2 認知心理測試................................................................................4
2.1.3 腦部影像技術.................................................6
2.2 擴散張量影像..........................................................10
2.3 神經纖維追蹤演算法.......................................13
2.3.1 FACT演算法....................................................................13
2.3.2 EZ-tracing演算法.................................................15
3. 研究方法......................................................................17
3.1 矩陣特徵值求解(eigensolvers)................................17
3.2 非等向性指標判定.........................................19
3.3 內積值(Inner product)判定..................................21
4. 實驗結果.....................................................23
4.1 64*64神經纖維追蹤....................................................23
4.2 128*128神經纖維追蹤..............................................26
5. 未來方向.....................................................29
參考文獻..................................................30
[1]李秀萍,「利用相似度決定白質神經纖維束成像之最佳擴散張量梯度數目」,國立台灣大學醫學工程學研究所碩士論文,民國九十四年六月。
[2]曹書萍,「擴散磁振造影於人腦語言神經纖維束之研究與應用」,國立楊明大生命科學院神經科學研究所碩士論文,民國九十六年七月。
[3]李超群, “阿茲海默症的神經放射診斷觀點”, 中華民國神經放射線醫學會會刊,vol. 31, pp.11-13, Dec 15 2006.
[4]嚴寶勝, “擴散張量影像技術在神經白質疾病的應用”, 中華民國神經放射線醫學會會刊,vol. 29, pp.11-14, Jun 15 2006.
[5]D. A. Hamstra, T. L. Chenevert, B. A. Moffat, T. D. Johnson, C. R. Meyer, S. K.Mukherji, D. J. Quint, S. S. Gebarski, X. Fan, C. I. Tsien, T. S. Lawrence, L.Junck, A. Rehemtulla, and B. D. Ross, “"Evaluation of the functional diffusionmap as an early biomarker of time-to-progression and overall survival inhigh-grade glioma”, Proc Natl Acad Sci U S A, vol. 102, pp. 16759-64, Nov 15 2005.
[6]S. Mori, B. J. Crain, V. P. Chacko, and P. C. van Zijl, “Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging”, Ann Neurol, vol. 45, pp. 265-9, Feb 1999.
[7]P. J. Basser, J. Mattiello, and D. LeBihan, “Estimation of the effective self-diffusion tensor from the NMR spin echo”, J Magn Reson B, vol. 103, pp. 247-54, Mar 1994.
[8]P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging”, Biophys J, vol. 66, pp. 259-67, Jan 1994.
[9]Fang-Ying Chiu, Wan-Yuo Guo, Kun-Hsien Chou, Ming-Teh Chen, Yi-Ping Chao, Hsiu-Mei Wu, Shih-Ya Lo, Donald Ming-Tak Ho, and C.-P. Lin, “Using Quantitative MR Diffusion Tensor Imaging to Predict Post-surgical Outcome of Brain Tumors: A Feasibility Study”, Proc. Intl. Soc. Mag. Reson. Med., vol.14, 2006.
[10]R. A. Kockro, L. Serra, Y. Tseng-Tsai, C. Chan, S. Yih-Yian, C. Gim-Guan, E.Lee, L. Y. Hoe, N. Hern, and W. L. Nowinski, “Planning and simulation of neurosurgery in a virtual reality environment”, Neurosurgery, vol. 46, pp.118-35, discussion 135-7, Jan 2000.
[11]S. Mori and P. B. Barker, “Diffusion magnetic resonance imaging: its principle and applications”, Anat Rec, vol. 257, pp. 102-9, Jun 15 1999.
[12]Pierpaoli C., and Basser P.J., “Toward a quantitative assessment of diffusion anisotropy”, Magnetic Resonance in Medicine, vol. 36, pp. 893-906, 1996.
[13]Mei Bai ,and Shuqian Luo, “Improved Fiber Tracking for Diffusion Tensor MRI”, Capital University of Medical Sciences, Beijing 100054, China
[14]Ching-Po Lin, Wen-Yih Isaac Tseng, Hui-Cheng Cheng, and Jyh-Horng Chen. “Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered Manganese enhanced optic tracts”. NeuroImage 14[5], 1035-1047. 2001.
[15]Chaorui Huang, Paul Mattis, and Per Julin, “Identifying functional imaging markers of mild cognitive impairment in early Alzheimer’s and Parkinson’s disease using multivariate analysis”, Clinical Neuroscience Research , 6, pp. 367–373, 2007
[16]Hiroshi Matsuda, “Role of Neuroimaging in Alzheimer's Disease, with Emphasis on Brain Perfusion SPECT”, Journal of Nuclear Medicine, Vol. 48, No. 8, pp. 1289-1300, Aug 2007.
[17]S. Mori, B. J. Crain, V. P. Chacko, and P. C. van Zijl, “Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging”, Ann Neurol, vol. 45, pp. 265-9, Feb 1999.
[18]S. Mori and P. C. van Zijl, “Fiber tracking: principles and strategies – a technical review”, NMR Biomed, vol. 15, pp. 468-80, Nov-Dec 2002.
[19]P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data”, Magn Reson Med, vol. 44, pp. 625-32, Oct 2000.
[20]Kenshi Terajima and Tsutomu Nakada, “ EZ-tracing: a new ready-to-use algorithm for magnetic resonance tractography”, Journal of Neuroscience Methods, vol.16[2], pp.147-155,2002.
[21]M. J. Bundgaard, L. Regeur, H. J. G. Gundersen, and B. Pakkenberg, “Size of neocortical neurons in control subjects and in Alzheimer's disease”, Journal of Anatomy, 198, pp. 481-489, Apr2001.
[22]Warkentin S, Erikson C, Janciauskiene S, and Minthon L, “Brain hemodynamic dysfunction is associated with slow processing speed in Alzheimer’s disease”
[23] Tilo T. J. Kircher, Michael J. Brammer, W. Levelt, Mathias Bartels and Philip K. McGuire, “Pausing for thought: engagement of left temporal cortex during pauses in speech”, NeuroImage, Vol. 21, Issue 1, pp. 84-90, Jan 2004.
[24]Angela L. Jefferson, PhD, David F. Tate, PhD, Athena Poppas, MD, Adam M. Brickman, PhD, Robert H. Paul, PhD, John Gunstad, PhD, and Ronald A. Cohen, PhD, “Lower Cardiac Output Is Associated with Greater White Matter Hyperintensities in Older Adults with Cardiovascular Disease”, Journal of the American Geriatrics Society, Vol. 55, Issue 7, pp. 1044-1048, July 2007.
[25]Julian N. Trollor, Perminder S. Sachdev, Walter Haindl, Henry Brodaty, Wei Wen, and Brenda M. Walker, “Regional cerebral blood flow deficits in mild Alzheimer’s disease using high resolution single photon emission computerized tomography”, Psychiatry and Clinical Neurosciences, 59, pp. 280–290, 2005.
[26]Perminder Sachdev, Henry Brodaty, David Cheang, and Stuart Cathcart, “Hippocampus and amygdale volumes in elderly schizophrenic patients as assessed by magnetic resonance imaging”, Psychiatry and Clinical Neurosciences, 54, pp. 105–112, 2000.
[27]JL Tanabe, D Amend, N Schuff, V DiSclafani, F Ezekiel, D Norman, G Fein and MW Weiner, “Tissue segmentation of the brain in Alzheimer disease”, American Journal of Neuroradiology, Vol. 18, Issue 1 , pp.115-123, 1997.
[28]Yuken Fukutani, Nigel J Cairns, Masaki Shiozawa, Kazuo Sasaki, Satoru Sudo, Kiminori Isaki, and Peter Lantos, “Neuroal loss and neurofibrillary degeneration in the hippocampal cortex in late-onest sporadic Alzheimer’s disease ”, Psychiatry and Clinical Neurosciences, 54, pp.523-529, 2000.
[29]Hock, C., Villringer, K., Muller-Spahn, F., Wenzel, R., Heekeren, H., Schuh-Hofer,S., Hofmann, M., Minoshima, S., Schwaiger, M., Dirnagl, U., and Villringer, A., “Decrease in parietal cerebral hemoglobin oxygenation during performance of a verbal fluency task in patients with Alzheimer´s disease monitored by means of near-infrared spectroscopy (NIRS) – correlation with simultaneously rCBF-PET measurements”, Brain Research, 755, pp. 293-303, 1997.
[30]Fang-Ying Chiu, Wan-Yuo Guo, Kun-Hsien Chou, Ming-Teh Chen, Yi-Ping Chao, Hsiu-Mei Wu, Shih-Ya Lo, Donald Ming-Tak Ho, and C.-P. Lin, “Using Quantitative MR Diffusion Tensor Imaging to Predict Post-surgical Outcome of Brain Tumors: A Feasibility Study”, Proc. Intl. Soc. Mag. Reson. Med., vol. 14 2006.
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