Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 生醫電子與資訊學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49675
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳志宏
dc.contributor.authorHsin-Chih Loen
dc.contributor.author羅新知zh_TW
dc.date.accessioned2021-06-15T11:41:18Z-
dc.date.available2019-10-14
dc.date.copyright2016-10-14
dc.date.issued2016
dc.date.submitted2016-08-15
dc.identifier.citation[1] LP and Charles D. Coryell, The magnetic properties and structure of hemoglobin and related substances. Science, 1936; pp. 488-489.
[2] Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. U. S. A., 1990; 87, 9868–9872.
[3] Malonek D., Grinvald A., Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science, 1996 Apr; 272(5261):551-4.
[4] Bandettini, P.A., Wong, E.C., Hinks, R.S., Tikofsky, R.S., Hyde, J.S., Time course EPI of human brain function during task activation. Magn. Reson. Med., 1992; 25, 390–397.
[5] Kwong,K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E.,Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., Cheng, H.-M., Brady, T.J., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc.Natl. Acad. Sci. U. S. A., 1992; 89, 5675–5679.
[6] Ogawa, S., Tank, D.W., Menon, R., Ellermann, J.M., Kim, S.G., Merkle, H., Ugurbil, K., Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A., 1992; 89, 5951–5955.
[7] Hyder, F., Rothman, D.L., Shulman, R.G., Total neuroenergetics support localized brain activity: implications for the interpretation of fMRI. Proc. Natl. Acad. Sci. U. S. A., 2002; 99, 10771–10776.
[8] Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., Neurophysiological investigation of the basis of the fMRI signal. Nature, 2001; 412, 150–157.
[9] Buxton, R.B., Wong, E.C., Frank, L.R., Dynamics of blood flow and oxygenation changes during brain activation: The balloon model. Magn. Reson. Med., 1998; 39, 855–864.
[10] Friston, K.J., Mechelli, A., Turner, R., Price, C.J., Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage, 2000; 12, 466–477.
[11] Kim, S.-G., Ogawa, S., Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J. Cereb. Blood Flow Metab., 2012; 32, 1188–1206.
[12] Scott A. Huettel, Allen W. Song, Gregory McCarthy, Functional Magnetic Resonance Imaging, 2009
[13] Logothetis, N.K., What we can do and what we cannot do with fMRI, Nature, 2008; 453, 869-878. doi:10.1038/nature06976
[14] Joseph B. Mandeville, John J. A. Marota, C. Ayata, Greg Zaharchuk, Michael A. Moskowitz, Bruce R. Rosen, Robert M. Weisskoff, Evidence of a Cerebrovascular Postarteriole Windkessel with Delayed Compliance. J Cereb Blood Flow Metab, 1999
[15] Haacke EM, Xu Y, Cheng YC, Reichenbach JR., Susceptibility weighted imaging (SWI). Magn Reson Med, 2004 Sep;52(3):612-8.
[16] Duyn JH, van Gelderen P, Li TQ, de Zwart JA, Koretsky AP, Fukunaga M, High-field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci USA,2007; 104:11796–11801
[17] Haacke E.M., Mittal S., Wu Z., Neelavalli J., Cheng Y.-C.N., Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1. AJNR, 2009, 30: 19-30
[18] de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med. 2010; 63: 194–206. doi: 10.1002/mrm.22187 PMID: 19953507
[19] Haacke E, Tang J, Neelavalli J, Cheng Y., Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. J Magn Reson Imaging. 2010; 32: 663–676. doi: 10.1002/jmri.22276 PMID:20815065
[20] Tang J, Liu S, Neelavalli J, Cheng Y., Improving susceptibility mapping using a threshold‐based Kspace/image domain iterative reconstruction approach. Magn Reson Med. 2013; 69: 1396–1407. doi: 10.1002/mrm.24384 PMID: 22736331
[21] Fan AP, Bilgic B, Gagnon L, Witzel T, Bhat H, Rosen BR, Quantitative oxygenation venography from MRI phase. Magn Reson Med. 2014; 72: 149–159. doi: 10.1002/mrm.24918 PMID: 24006229
[22] Zhang J, Liu T, Gupta A, Spincemaille P, Nguyen TD, Wang Y., Quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) using quantitative susceptibility mapping (QSM). Magn Reson Med. 2015; 74: 945–952. doi: 10.1002/mrm.25463 PMID: 25263499
[23] Xu B, Liu T, Spincemaille P, Prince M, Wang Y., Flow compensated quantitative susceptibility mapping for venous oxygenation imaging. Magn Reson Med. 2014; 72: 438–445. doi: 10.1002/mrm.24937 PMID: 24006187
[24] Hsieh M-C, Tsai C-Y, Liao M-C, Yang J-L, Su C-H, Chen J-H, Quantitative Susceptibility Mapping-Based Microscopy of Magnetic Resonance Venography (QSM-mMRV) for In Vivo Morphologically and Functionally Assessing Cerebromicrovasculature in Rat Stroke Model. PLoS ONE 2016; 11(3): e0149602. doi:10.1371/journal.pone.0149602
[25] Mansfield P., Real-time echo-planar imaging by NMR. Br Med Bull, 1984;40:187–190.
[26] Hongfu Sun, Alan H. Wilman, Quantitative Susceptibility Mapping Using Single-Shot Echo-Planar Imaging. Magnetic Resonance in Medicine, 2015; 73:1932–1938.
[27] Balla DZ, Panchuelo RMS, Wharton SJ, Hagberg GE, Scheffler K, Francis ST, Bowtell RW. Experimental investigation of the relation between gradient echo BOLD fMRI contrast and underlying susceptibility changes at 7T. In Proceedings of the 21st Annual Meeting of ISMRM, Salt Lake City, Utah, USA, 2013
[28] Balla D, Ehses P, Pohmann R, Mirkes C, Shajan G, Scheffler K, Bowtell R. Functional QSM at 9.4T with single echo gradient-echo and EPI acquisition. In 2nd Workshop on MRI Phase Contrast & Quantitative Susceptibility Mapping (QSM). Ithaca, New York USA, 2013
[29] He X, Yablonskiy DA, Quantitative BOLD: Mapping of human cerebral deoxygenated blood volume and oxygen extraction fraction: Default state. Magn Reson Med, 2007; 57:115–126.
[30] Ito H, Ibaraki M, Kanno I, Fukuda H, Miura S, Changes in cerebral blood flow and cerebral oxygen metabolism during neural activation measured by positron emission tomography: Comparison with blood oxygenation level-dependent contrast measured by functional magnetic resonance imaging. J Cereb Blood Flow Metab,2005; 25:371–377.
[31] Li D, Wang Y, Waight DJ., Blood oxygen saturation assessment in vivo using T2* estimation. Magn Reson Med. 1998; 39: 685–690. doi: 10.1002/mrm.1910390503 PMID: 9581597
[32] Sedlacik J, Rauscher A, Reichenbach JR., Obtaining blood oxygenation levels from MR signal behavior in the presence of single venous vessels. Magn Reson Med. 2007; 58: 1035–1044. doi: 10.1002/mrm. 21283 PMID: 17969121
[33] Haacke EM, Lai S, Reichenbach JR, Kuppusamy K, Hoogenraad FG, Takeichi H, In vivo measurement of blood oxygen saturation using magnetic resonance imaging: A direct validation of the blood oxygen level‐dependent concept in functional brain imaging. Hum Brain Mapp. 1997; 5: 341–346. doi: 10.1002/(SICI)1097-0193(1997)5:5<341::AID-HBM2>3.0.CO;2-3 PMID: 20408238
[34] Fern&aacute;ndez-Seara MA, Techawiboonwong A, Detre JA, Wehrli FW., MR susceptometry for measuring global brain oxygen extraction. Magn Reson Med. 2006; 55: 967–973. doi: 10.1002/mrm.20892 PMID: 16598726
[35] Fan AP, Benner T, Bolar DS, Rosen BR, Adalsteinsson E., Phase-based regional oxygen metabolism (PROM) using MRI. Magn Reson Med. 2012; 67: 669–678. doi: 10.1002/mrm.23050 PMID: 21713981
[36] Driver ID, Wharton SJ, Croal PL, Bowtell R, Francis ST, Gowland PA., Global intravascular and local hyperoxia contrast phase-based blood oxygenation measurements. NeuroImage. 2014; 101: 458–465. doi: 10.1016/j.neuroimage.2014.07.050 PMID: 25091128
[37] Krishnamurthy LC, Liu P, Ge Y, Lu H., Vessel-specific quantification of blood oxygenation with T2-relaxation-under-phase-contrast MRI. Magn Reson Med. 2014; 71: 978–989. doi: 10.1002/mrm.24750 PMID: 23568830
[38] Marques J, Maddage R, Mlynarik V, Gruetter R., On the origin of the MR image phase contrast: an in vivo MR microscopy study of the rat brain at 14.1 T. NeuroImage. 2009; 46: 345–352. doi: 10.1016/j. neuroimage.2009.02.023 PMID: 19254768
[39] Lee J, Hirano Y, Fukunaga M, Silva AC, Duyn JH., On the contribution of deoxy-hemoglobin to MRI gray–white matter phase contrast at high field. NeuroImage. 2010; 49: 193–198. doi: 10.1016/j. neuroimage.2009.07.017 PMID: 19619663
[40] Menon RS, Postacquisition suppression of large-vessel BOLD signals in high-resolution fMRI. Magn Reson Med, 2002; 47:1–9.
[41] Nencka AS, Rowe DB, Reducing the unwanted draining vein BOLD contribution in fMRI with statistical post-processing methods. Neuroimage, 2007; 37:177–188.
[42] Petridou N, Schafer A, Gowland P, Bowtell R, Phase vs. magnitude information in functional magnetic resonance imaging time series: Toward understanding the noise. Magn Reson Imaging, 2009; 27:1046–1057.
[43] Hagberg GE, Bianciardi M, Brainovich V, Cassara AM, Maraviglia B, Phase stability in fMRI time series: Effect of noise regression, off-resonance correction and spatial filtering techniques. Neuroimage, 2012; 59:3748–3761.
[44] Hahn AD, Nencka AS, Rowe DB, Improving robustness and reliability of phase-sensitive fMRI analysis using temporal off-resonance alignment of single-echo timeseries (TOAST). Neuroimage, 2009; 44:742–752.
[45] Liu T, Spincemaille P, de Rochefort L, Kressler B, Wang Y., Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med. 2009; 61: 196–204. doi: 10.1002/mrm.21828 PMID: 19097205
[46] Kressler B, de Rochefort L, Liu T, Spincemaille P, Jiang Q, Wang Y., Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps. IEEE Trans Med Imaging. 2010; 29: 273–281. doi: 10.1109/TMI.2009.2023787 PMID: 19502123
[47] Wharton S, Bowtell R., Whole-brain susceptibility mapping at high field: a comparison of multiple-and single-orientation methods. NeuroImage. 2010; 53: 515–525. doi: 10.1016/j.neuroimage.2010.06.070 PMID: 20615474
[48] Liu T, Liu J, de Rochefort L, Spincemaille P, Khalidov I, Ledoux JR, Morphology enabled dipole inversion (MEDI) from a single‐angle acquisition: comparison with COSMOS in human brain imaging. Magn Reson Med. 2011; 66: 777–783. doi: 10.1002/mrm.22816 PMID: 21465541
[49] Bianciardi, M., van Gelderen, P., Duyn, J.H., Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T. Human Brain Mapping, 2014; 35 (5), 2191–2205.
[50] Chen, Z., Liu, J., Calhoun, V.D., Susceptibility-based functional brain mapping by 3D deconvolution of an MR-phase activation map. Journal of Neuroscience Methods, 2013; 216, 33–42.
[51] Arja, S.K., Feng, Z.M., Chen, Z.K., Caprihan, A., Kiehl, K.A., Adali, T., Calhoun, V.D., Changes in fMRI magnitude data and phase data observed in block-design and event-related tasks. NeuroImage, 2010; 49, 3149–3160.
[52] Hagberg, G.E., Bianciardi, M., Brainovich, V., Cassara, A.M., Maraviglia, B., The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents. Magn. Reson. Imaging, 2008; 26, 1026–1040.
[53] Rowe, D.B., Logan, B.R., A complex way to compute fMRI activation. NeuroImage, 2004; 23, 1078–1092.
[54] Rowe, D.B., Modeling both the magnitude and phase of complex-valued fMRI data. NeuroImage, 2005; 25, 1310–1324.
[55] Rowe, D.B., Meller, C.P., Hoffmann, R.G., Characterizing phase-only fMRI data with an angular regression model. J. Neurosci. Methods, 2007; 161, 331–341.
[56] Tomasi, D.G., Caparelli, E.C., Macrovascular contribution in activation patterns of working memory. J. Cereb. Blood Flow Metab., 2007; 27, 33–42.
[57] Lo H-C, Hsieh M-C, Chen D-Y, Chen K-H, Chen J-H, Quantitative Susceptibility Functional MRI (QS-fMRI) of Rat Brain during Flashing Light Stimulation, ICMRBS, 2016
[58] Grabowski, T., and Damasio, A., Investigating language with functional neuroimaging. San Diego, CA, US: Academic Press., 2000; 14, 425-461.
[59] Chen D-Y, Chen K-H, Liang K-C, Visual BOLD responses of dexmedetomidine-anesthetized rats to flashing light stimulation in an fMRI study, Neuroscience, 2016
[60] Soon-Cheol Chung, Gye-Rae Tack, Bongsoo Lee, Gwang-Moon Eom, Soo-Yeol Lee, Jin-Hun Sohn, The effect of 30% oxygen on visuospatial performance and brain activation: An fMRI study. Brain and Cognition, 2004; 56, 579-285. doi:10.1016/j.bandc.2004.07.005
[61] Mark Jenkinson, Fast, Automated, N-Dimensional Phase-Unwrapping Algorithm, Magnetic Resonance in Medicine, 2003; 49:193–197
[62] Abdul-Rahamn HS, Gdeisat MA, Burton DR, Lalor MJ, Lilley F, Moore CJ., Fast and robust three-dimensional best path phase unwrapping algorithm. Appl Opt., 2007; 46: 6623–6635. doi: 10.1364/AO.46.006623 PMID: 17846656
[63] Smith, S.M., Fast robust automated brain extraction. Humman Brain Mapping, 2002; 17, 143–155
[64] Liu T, Khalidov I, de Rochefort L, Spincemaille P, Liu J, Tsiouris AJ, Wang Y., A novel background field removal method for MRI using projection onto dipole fields (PDF), NMR Biomed, 2001; 24(9):1129-36. doi: 10.1002/nbm.1670.
[65] Nocedal J, Wright S., Numerical Optimization. 2nd ed. Springer; 2006.
[66] Roemer P, Edelstein W, Hayes C, Souza S, Mueller O. The NMR phased array., Magn Reson Med., 1990; 16: 192–225. PMID: 2266841
[67] Hammond KE, Lupo JM, Xu D, Metcalf M, Kelley DAC, Pelletier D, Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. NeuroImage. 2008; 39: 1682–1692. doi: 10.1016/j. neuroimage.2007.10.037 PMID: 18096412
[68] M A Bernstein, K F King and X J Zhou., Handbook of MRI Pulse Sequences., San Diego: Elsevier Academic Press, 2004; 960. ISBN 0-1209-2861-2.
[69] R C Weast, M J Astle., Handbook of Chemistry and Physics., Boca Raton: CRC Press, 1982; E66. ISBN 0-8493-0463-6.
[70] Li L, Leigh JS., Quantifying arbitrary magnetic susceptibility distributions with MR., Magn. Reson. Med., 2004; 51(5): 1077–1082.
[71] Haacke EM, Brown RW, Thompson MR., Objects in external fields: the Lorentz sphere. Magnetic Resonance Imaging: Physical Principles and Sequence Design., Wiley-Liss: New York, 1999; pp. 749–757.
[72] Jackson JD., Classical Electrodynamics, 3rd edn. Wiley: New York, 1999.
[73] Moon TK, Stirling WC., Pseudoinverses and the SVD. Mathematical Methods and Algorithms for Signal Processing., Prentice Hall: Upper Saddle River, New Jersey, 2000; pp. 116–117.
[74] Salomir R, de Senneville BD, Moonen CT., A fast calculation method for magnetic field inhomogeneity due to an arbitrary distribution of bulk susceptibility. Concept Magn Reson B. 2003; 19: 26–34.
[75] Wu B, Li W, Guidon A, Liu C., Whole brain susceptibility mapping using compressed sensing. Magn Reson Med. 2012; 67: 137–147. doi: 10.1002/mrm.23000 PMID: 21671269
[76] Liu T, Xu W, Spincemaille P, Avestimehr AS, Wang Y., Accuracy of the morphology enabled dipole inversion (MEDI) algorithm for quantitative susceptibility mapping in MRI. IEEE Trans Med Imaging. 2012; 31: 816–824. doi: 10.1109/TMI.2011.2182523 PMID: 22231170
[77] Schweser F, Sommer K, Deistung A, Reichenbach JR., Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. NeuroImage. 2012; 62: 2083–2100. doi: 10. 1016/j.neuroimage.2012.05.067 PMID: 22659482
[78] Weisskoff RM, Kiihne S., MRI susceptometry: Image‐based measurement of absolute susceptibility of MR contrast agents and human blood. Magn Reson Med., 1992; 24: 375–383. PMID: 1569876
[79] S Ogawa, TM Lee, B Barrere, The sensitivity of magnetic resonance image signals of a rat brain to changes in the cerebral venous blood oxygenation. Magnetic resonance in medicine, 1993
[80] Yimin Shen, Zhifeng Kou, Christian W. Kreipke, Theodor Petrov, Jiani Hu, E. Mark Haacke, In vivo measurement of tissue damage, oxygen saturation changes and blood flow changes after experimental traumatic brain injury in rats using susceptibility weighted imaging. Magnetic Resonance Imaging, 2007
[81] Lin W, Paczynski RP, Celik A, Hsu CY, Powers WJ., Experimental hypoxemic hypoxia: effects of variation in hematocrit on magnetic resonance T2*-weighted brain images. J Cereb Blood Flow Metab., 1998; 18: 1018–1021. PMID: 9740105
[82] Barbee JH, Cokelet GR., The Fahraeus effect. Microvasc Res. 1971
[83] Thomas Mueller, What is the thalamus in zebrafish? Front. Neurosci., 07 May 2012
[84] Polyak, S., The Vertebrate Visual System. University of Chicago Press, Chicago, 1957
[85] Sefton, A.J., Dreher, B., Harvey, A., Visual system. In: Paxinos, G. (Ed.), The Rat Nervous System. Elsevier Academic Press, San Diego, 2004
[86] Lamme VA, Roelfsema PR., The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 2000; 23(11):571-9.
[87] Orban GA, Van Essen D, Vanduffel W., Comparative mapping of higher visual areas in monkeys and humans. Trends in Cognitive Sciences., 2004; 8: 315-24. PMID 15242691 DOI: 10.1016/j.tics.2004.05.009
[88] Sahibzada, N., Dean, P., Redgrave, P., Movements resembling orientation or avoidance elicited by electrical stimulation of the superior colliculus in rats. J. Neurosci., 1986; 6, 723–733.
[89] Cooper, B.G., Miya, D.Y., Mizumori, S.J., Superior colliculus and active navigation: role of visual and non-visual cues in controlling cellular representations of space. Hippocampus, 1998; 8, 340–372.
[90] McHaffie, J.G., Stein, B.E., Eye movements evoked by electrical stimulation in the superior colliculus of rats and hamsters. Brain Res, 1982; 247, 243–253.
[91] King, S.M., Escape-related behaviours in an unstable, elevated and exposed environment. II. Long-term sensitization after repetitive electrical stimulation of the rodent midbrain defence system. Behav. Brain Res., 1999; 98, 127–142.
[92] Vargas, L.C., Marques, T.A., Schenberg, L.C., Micturition and defensive behaviors are controlled by distinct neural networks within the dorsal periaqueductal gray and deep gray layer of the superior colliculus of the rat. Neurosci. Lett., 2000; 280, 45–48.
[93] Stein, B.E., Organization of the rodent superior colliculus: some comparisons with other mammals. Behav Brain Res, 1981; 3, 175–188.
[94] Pawela CP, Hudetz AG, Ward BD, Schulte ML, Li R, Kao DS, Modeling of region-specific fMRI BOLD neurovascular response functions in rat brain reveals residual differences that correlate with the differences in regional evoked potentials. Neuroimage. 2008;41(2):525-34. doi: 10.1016/j.neuroimage.2008.02.022. Epub 2008 Mar 4.
[95] Eszter A. Papp, Trygve B. Leergaard, Evan Calabrese, G. Allan Johnson, Jan G. Bjaalie, Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage, 2014; 97; 374-38.
[96] Weili Lin, Richard P. Paczynski, Azim Celik, Karthikeyan Kuppusamy, Chung Y. Hsu, William J. Powers, Experimental hypoxemic hypoxia: Changes in R2* of brain parenchyma accurately reflect the combined effects of changes in arterial and cerebral venous oxygen saturation. Magnetic resonance in medicine, 1998
[97] E Vovenko, Distribution of oxygen tension on the surface of arterioles, capillaries and venules of brain cortex and in tissue in normoxia:an experimental study on rats. Pflgers Archiv, 1999
[98] P W McCormick, M Stewart, M G Goetting, G Balakrishnan, Regional cerebrovascular oxygen saturation measured by optical spectroscopy in humans. Stroke, 1991
[99] Cai K, Shore A, Singh A, Haris M, Hiraki T, Waghray P, Reddy D, Greenberg JH, Reddy R., Blood oxygen level dependent angiography(BOLDangio) and its potential applications in cancer research. NMR Biomed 2012;25:1125–1132.
[100] Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, Healy MJ, Gibbs JM, Wise RJ, Hatazawa J, Herold S, Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age. Brain. 1990 Feb;113 ( Pt 1):27-47.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49675-
dc.description.abstract血氧濃度相依功能性磁振影像已被廣泛應用於量測大腦活化反應之工具,它可以定性地觀察大腦在刺激下活化反應區的血氧濃度變化。近年來,磁化率定量影像技術被提出用來計算活體中的磁化率特性,更能進一步估算出靜脈血氧飽和濃度。本研究應用磁化率定量技術於大鼠視覺閃光刺激之大腦功能性研究模型,建立磁化率定量功能性磁振影像之實驗流程,以提供大腦功能性研究定量的生理資訊。
  本研究採用5赫茲之閃光刺激進行功能性磁振掃描,並將掃描所得之相位影像計算成磁化率影像,接著將磁化率定量影像與強度影像分別進行相關性之群組分析來觀察其大腦活化區。此外,我們也改變了氧氣濃度來探討不同狀態下之磁化率變化,將其計算成靜脈血氧飽和濃度,並校正部分體積效應所產生的定量誤差。
  結果顯示,在視覺刺激下,外側膝狀核和四疊體上丘皆有強烈的大腦活化反應。比較兩種影像,由於局部帶氧血紅素增加使得磁化率下降,強度影像訊號上升,故可觀察到磁化率與血氧濃度相依訊號有著相反的趨勢。在呼吸30%氧氣的狀態下,其磁化率與靜脈血氧飽和濃度在刺激開啟與關閉之平均值分別為148.00±3.90 ppb、153.00±4.80 ppb與83.62±0.44%、82.99±0.53%。在呼吸100%氧氣下,其磁化率與靜脈血氧飽和濃度在刺激開啟與關閉之平均之值別為109.50±6.00 ppb、115.20±6.30 ppb與87.93±0.67%、87.30±0.70%。同樣地,在兩種不同氧氣濃度狀態下,磁化率改變量均大幅提升為血氧濃度相依功能性磁振影像之訊號改變量的4倍。
  本研究成功地驗證磁化率定量功能性磁振影像技術應用於視覺刺激之動物功能性磁振影像之可行性,並藉以定量地觀察鼠大腦血氧濃度於視覺刺激下之變化。其中,磁化率之血流動力學反應有血氧代謝之反應趨勢,以及類似於大腦血流之反應趨勢,此結果還需未來更進一步的驗證。如此一來,藉由其可定量化之優勢,且可同步提供磁化率以及一般血氧濃度相依功能性磁振訊號,此技術將有潛力成為定量研究大腦功能性影像之有利輔助工具。
zh_TW
dc.description.abstractBOLD-fMRI has been used to measure brain activity by detecting associated changes in oxygenation fluctuation. Recently, quantitative susceptibility mapping (QSM) has been proposed to measure susceptibility property and further calculate to venous oxygen saturation (SvO2) using phase information. The purpose of this study is to apply the QSM technique to BOLD-fMRI during visual stimulation to provide quantitative and physiological information while the brain processing.
In this study, we used 5 Hz flashing stimulus during fMRI acquisition. Phase information was extracted to calculate QSM, and the activation map of both magnitude (conventional BOLD) and QSM time-series were calculated in group analysis. Furthermore, we changed inhaled oxygenation levels (30% and 100%) to observe rat brain venous susceptibility changes to quantify SvO2; and intended to calibrate the underestimated susceptibility caused by partial volume effect.
The flashing light stimulation evoked strong responses on lateral geniculate nucleus (LGN) and superior colliculus (SC) on both BOLD-fMRI and QS-fMRI results. Comparing to conventional BOLD-fMRI time course, QS-fMRI signal was introduced from the compensation of oxygenated hemoglobin after neural activity and causes a reduced signal change due to susceptibility in local cerebral regions, where BOLD response would show accordingly enhanced signal of EPI magnitude images. During 30 % oxygen inhalation, the calibrated susceptibility was 148.00 ± 3.90 ppb while the task is on, and susceptibility was 153.00 ± 4.80 ppb while the task is off. The calibrated SvO2 was 83.62 ± 0.44 % while the task is on, and SvO2 was 82.99 ± 0.53 % while the task is off. During 100% oxygen inhalation, the calibrated susceptibility was 109.50 ± 6.00 ppb while the task is on, and susceptibility was 115.20 ± 6.30 ppb while the task is off. The calibrated SvO2 was 87.93±0.67% while the task is on, and SvO2 was 87.30±0.70% while the task is off. Interestingly, susceptibility change of QS-fMRI is 4 times larger than BOLD signal change in both inhalation oxygenation conditions; indicated the high sensitivity of QS-fMRI.
To summarize, we here demonstrated the feasibility of animal QS-fMRI technique to calculate SvO2 during functional task. According to previous studies, we suggested susceptibility hemodynamic response of was similar/dominated to both the responses of cerebral metabolism of oxygenation and cerebral blood flow. With further validation, the quantitative QS-fMRI technique could be a powerful tool for functional studies.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T11:41:18Z (GMT). No. of bitstreams: 1
ntu-105-R01945015-1.pdf: 3548883 bytes, checksum: 28be78e8a792d14a81476721c5e7628c (MD5)
Previous issue date: 2016
en
dc.description.tableofcontents口試委員會審定書 I
誌謝 II
中文摘要 III
ABSTRACT IV
CONTENTS VI
LIST OF FIGURES VIII
Chapter 1 緒論 1
1.1 血氧濃度相依效應 1
1.2 血氧濃度相依功能性磁振影像 6
1.3 血流動力學反應 9
1.4 磁化率定量影像 10
1.5 二維磁化率定量影像 13
1.6 定量功能性磁振影像 16
1.7 研究動機與目的 18
Chapter 2 實驗材料與實驗方法 19
2.1 實驗設計 19
2.1.1 視覺刺激參數 19
2.1.2 磁振影像掃描參數 21
2.1.3 實驗動物準備 22
2.2 磁化率定量功能性影像之分析步驟 22
2.2.1 陣列線圈影像重組 25
2.2.2 相位展開 26
2.2.3 去除背景磁場 27
2.2.4 L1正規化分析 29
2.2.5 功能性影像結果分析 30
Chapter 3 實驗結果 33
3.1 功能性磁振影像之結果 33
3.2 反應區圖譜位置對照 37
3.3 磁化率改變量對BOLD改變量之散布圖 38
3.4 靜脈血氧飽和濃度之結果 40
Chapter 4 討論、結論與未來展望 42
4.1 討論 42
4.1.1 功能性刺激的選擇 42
4.1.2 磁化率定量影像之影像厚度 45
4.1.3 部分體積效應之校正 50
4.1.4 磁化率定量影像之血流動力學反應 56
4.2 結論與未來展望 58
4.2.1 結論 58
4.2.2 未來展望 59
參考文獻 61
dc.language.isozh-TW
dc.subject靜脈血氧濃度zh_TW
dc.subject磁化率定量影像zh_TW
dc.subject功能性磁振影像zh_TW
dc.subject視覺刺激zh_TW
dc.subjectvisual stimulien
dc.subjectvenous oxygen saturationen
dc.subjectsusceptibility quantitative mappingen
dc.subjectfunctional MRIen
dc.title磁化率定量功能性磁振影像於鼠腦視覺刺激之研究zh_TW
dc.titleQuantitative Susceptibility Functional MRI (QS-fMRI) of Rat Brain during Visual Stimulationen
dc.typeThesis
dc.date.schoolyear104-2
dc.description.degree碩士
dc.contributor.oralexamcommittee張程,梁庚辰,孫啟光,嚴震東,吳昌衛
dc.subject.keyword磁化率定量影像,功能性磁振影像,視覺刺激,靜脈血氧濃度,zh_TW
dc.subject.keywordsusceptibility quantitative mapping,functional MRI,visual stimuli,venous oxygen saturation,en
dc.relation.page72
dc.identifier.doi10.6342/NTU201602366
dc.rights.note有償授權
dc.date.accepted2016-08-16
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept生醫電子與資訊學研究所zh_TW
顯示於系所單位:生醫電子與資訊學研究所

文件中的檔案:
檔案 大小格式 
ntu-105-1.pdf
  未授權公開取用
3.47 MBAdobe PDF
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved