請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80383完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳志宏(Jyh-Horng Chen) | |
| dc.contributor.author | Chia-Ming Shih | en |
| dc.contributor.author | 石家銘 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:05:32Z | - |
| dc.date.available | 2021-09-11 | |
| dc.date.available | 2022-11-24T03:05:32Z | - |
| dc.date.copyright | 2021-09-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-09 | |
| dc.identifier.citation | 1. Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. proceedings of the National Academy of Sciences, 1990. 87(24): p. 9868-9872. 2. Ogawa, S., et al., Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences, 1992. 89(13): p. 5951-5955. 3. Kim, S.-G. and S. Ogawa, Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. Journal of Cerebral Blood Flow Metabolism, 2012. 32(7): p. 1188-1206. 4. Hall, C.N., et al., Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience. 2016, The Royal Society. 5. Barth, M. and B.A. Poser, Advances in high-field BOLD fMRI. Materials, 2011. 4(11): p. 1941-1955. 6. Tsvetanov, K.A., R.N. Henson, and J.B. Rowe, Separating vascular and neuronal effects of age on fMRI BOLD signals. Philosophical Transactions of the Royal Society B, 2021. 376(1815): p. 20190631. 7. Satoh, M., et al., The Effect of Motion Artifacts on Near-Infrared Spectroscopy (NIRS) Data and Proposal of a Video-NIRS System. Dementia and geriatric cognitive disorders extra, 2017. 7(3): p. 406-418. 8. Gore, J.C., Principles and practice of functional MRI of the human brain. The Journal of clinical investigation, 2003. 112(1): p. 4-9. 9. Huettel, S.A., A.W. Song, and G. McCarthy, Functional magnetic resonance imaging. Vol. 1. 2004: Sinauer Associates Sunderland, MA. 10. Bianciardi, M., P. van Gelderen, and J.H. Duyn, Investigation of BOLD fMRI resonance frequency shifts and quantitative susceptibility changes at 7 T. Human brain mapping, 2014. 35(5): p. 2191-2205. 11. Jenkinson, M. and M. Chappell, Introduction to neuroimaging analysis. 2018: Oxford University Press. 12. Ruetten, P.P., J.H. Gillard, and M.J. Graves, introduction to Quantitative Susceptibility Mapping and Susceptibility weighted imaging. The British journal of radiology, 2019. 92(1101): p. 20181016. 13. Deistung, A., F. Schweser, and J.R. Reichenbach, Overview of quantitative susceptibility mapping. NMR in Biomedicine, 2017. 30(4): p. e3569. 14. Haacke, E.M., et al., Quantitative susceptibility mapping: current status and future directions. Magnetic resonance imaging, 2015. 33(1): p. 1-25. 15. Reichenbach, J., et al., Quantitative susceptibility mapping: concepts and applications. Clinical neuroradiology, 2015. 25(2): p. 225-230. 16. Balla, D., et al. Functional QSM at 9.4 T with single echo gradient-echo and EPI acquisition. in 2nd International Workshop on MRI Phase Contrast Quantitative Susceptibility Mapping (QSM 2013). 2013. 17. Wharton, S. and R. Bowtell, Whole-brain susceptibility mapping at high field: a comparison of multiple-and single-orientation methods. Neuroimage, 2010. 53(2): p. 515-525. 18. Liu, T., et al., Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping. Radiology, 2012. 262(1): p. 269-278. 19. Haacke, E., et al., Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. Journal of Magnetic Resonance Imaging, 2010. 32(3): p. 663-676. 20. Bilgic, B., et al., MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage, 2012. 59(3): p. 2625-2635. 21. Acosta-Cabronero, J., et al., In vivo quantitative susceptibility mapping (QSM) in Alzheimer's disease. PloS one, 2013. 8(11): p. e81093. 22. Lotfipour, A.K., et al., High resolution magnetic susceptibility mapping of the substantia nigra in Parkinson's disease. Journal of Magnetic Resonance Imaging, 2012. 35(1): p. 48-55. 23. Ng, A.C., et al., Iron accumulation in the basal ganglia in Huntington's disease: cross-sectional data from the IMAGE-HD study. Journal of Neurology, Neurosurgery Psychiatry, 2016. 87(5): p. 545-549. 24. Kiselev, V.G., On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2001. 46(6): p. 1113-1122. 25. Bonekamp, D., et al., Susceptibility‐based analysis of dynamic gadolinium bolus perfusion MRI. Magnetic resonance in medicine, 2015. 73(2): p. 544-554. 26. Guan, X., et al., Regionally progressive accumulation of iron in Parkinson's disease as measured by quantitative susceptibility mapping. NMR in Biomedicine, 2017. 30(4): p. e3489. 27. Balla, D.Z., et al., Functional quantitative susceptibility mapping (fQSM). Neuroimage, 2014. 100: p. 112-124. 28. Chen, Z., J. Liu, and V.D. Calhoun, Susceptibility-based functional brain mapping by 3D deconvolution of an MR-phase activation map. Journal of neuroscience methods, 2013. 216(1): p. 33-42. 29. Özbay, P.S., et al., Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: A group level comparison with BOLD fMRI and PET. Neuroimage, 2016. 137: p. 52-60. 30. Sun, H., P. Seres, and A. Wilman, Structural and functional quantitative susceptibility mapping from standard fMRI studies. NMR in Biomedicine, 2017. 30(4): p. e3619. 31. Chen, Z., et al., High-resolution human brain functional χ mapping reveals focal and bidirectional BOLD responses. Biomedical Physics Engineering Express, 2017. 3(1): p. 015027. 32. Costagli, M., et al., Quantitative Susceptibility Mapping of Brain Function During Auditory Stimulation. IEEE Transactions on Radiation and Plasma Medical Sciences, 2019. 3(4): p. 516-522. 33. Christen, T., D. Bolar, and G. Zaharchuk, Imaging brain oxygenation with MRI using blood oxygenation approaches: methods, validation, and clinical applications. American journal of neuroradiology, 2013. 34(6): p. 1113-1123. 34. Fan, A.P., et al., Quantitative oxygenation venography from MRI phase. Magnetic resonance in medicine, 2014. 72(1): p. 149-159. 35. Jain, V., et al., High temporal resolution in vivo blood oximetry via projection‐based T2 measurement. Magnetic resonance in medicine, 2013. 70(3): p. 785-790. 36. Rodgers, Z.B., et al., Rapid T2-and susceptometry-based CMRO2 quantification with interleaved TRUST (iTRUST). Neuroimage, 2015. 106: p. 441-450. 37. Fan, A.P., et al., Regional quantification of cerebral venous oxygenation from MRI susceptibility during hypercapnia. Neuroimage, 2015. 104: p. 146-155. 38. Zhang, J., et al., Quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) using quantitative susceptibility mapping (QSM). Magnetic resonance in medicine, 2015. 74(4): p. 945-952. 39. Hsieh, M.C., et al., Investigating hyperoxic effects in the rat brain using quantitative susceptibility mapping based on MRI phase. Magnetic Resonance in Medicine, 2017. 77(2): p. 592-602. 40. Pawela, C.P., et al., 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): p. 525-534. 41. Weber, R., et al., A fully noninvasive and robust experimental protocol for longitudinal fMRI studies in the rat. Neuroimage, 2006. 29(4): p. 1303-1310. 42. Zhao, F., et al., BOLD study of stimulation-induced neural activity and resting-state connectivity in medetomidine-sedated rat. Neuroimage, 2008. 39(1): p. 248-260. 43. Roemer, P.B., et al., The NMR phased array. Magnetic resonance in medicine, 1990. 16(2): p. 192-225. 44. Hammond, K.E., et al., 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(4): p. 1682-1692. 45. Smith, S.M., Fast robust automated brain extraction. Human brain mapping, 2002. 17(3): p. 143-155. 46. Robinson, S.D., et al., An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR in Biomedicine, 2017. 30(4): p. e3601. 47. Schweser, F., A. Deistung, and J.R. Reichenbach, Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM). Zeitschrift für medizinische Physik, 2016. 26(1): p. 6-34. 48. Jenkinson, M., Fast, automated, N‐dimensional phase‐unwrapping algorithm. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2003. 49(1): p. 193-197. 49. Bernstein, M.A., K.F. King, and X.J. Zhou, Handbook of MRI pulse sequences. 2004: Elsevier. 50. Liu, T., et al., A novel background field removal method for MRI using projection onto dipole fields. NMR in Biomedicine, 2011. 24(9): p. 1129-1136. 51. Hsieh, M.-C., et al., 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): p. e0149602. 52. Liu, C., et al., Susceptibility‐weighted imaging and quantitative susceptibility mapping in the brain. Journal of magnetic resonance imaging, 2015. 42(1): p. 23-41. 53. Shmueli, K., et al., Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2009. 62(6): p. 1510-1522. 54. Haacke, E.M., et al., Imaging iron stores in the brain using magnetic resonance imaging. Magnetic resonance imaging, 2005. 23(1): p. 1-25. 55. de Rochefort, L., et al., Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 2010. 63(1): p. 194-206. 56. Liu, T., et al., Morphology enabled dipole inversion (MEDI) from a single‐angle acquisition: comparison with COSMOS in human brain imaging. Magnetic resonance in medicine, 2011. 66(3): p. 777-783. 57. Nocedal, J., Numerical Optimization/Jorge Nocedal. 2006, Springer Science+ Business Media.–2nd ed. LLC. 58. Haacke, E.M. and J.R. Reichenbach, Susceptibility weighted imaging in MRI: basic concepts and clinical applications. 2014: John Wiley Sons. 59. Ogawa, S., T. Lee, and 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. 29(2): p. 205-210. 60. Shen, Y., et al., 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. 25(2): p. 219-227. 61. Lin, W., et al., Experimental hypoxemic hypoxia: effects of variation in hematocrit on magnetic resonance T2*-weighted brain images. Journal of Cerebral Blood Flow Metabolism, 1998. 18(9): p. 1018-1021. 62. Weisskoff, R.M. and S. Kiihne, MRI susceptometry: image‐based measurement of absolute susceptibility of MR contrast agents and human blood. Magnetic resonance in medicine, 1992. 24(2): p. 375-383. 63. Hatori, M. and S. Panda, The emerging roles of melanopsin in behavioral adaptation to light. Trends in molecular medicine, 2010. 16(10): p. 435-446. 64. Cai, K., et al., Blood oxygen level dependent angiography (BOLDangio) and its potential applications in cancer research. NMR in biomedicine, 2012. 25(10): p. 1125-1132. 65. Lau, C., et al., BOLD responses in the superior colliculus and lateral geniculate nucleus of the rat viewing an apparent motion stimulus. Neuroimage, 2011. 58(3): p. 878-884. 66. Lau, C., et al., BOLD temporal dynamics of rat superior colliculus and lateral geniculate nucleus following short duration visual stimulation. PLoS One, 2011. 6(4): p. e18914. 67. Gesnik, M., et al., 3D functional ultrasound imaging of the cerebral visual system in rodents. NeuroImage, 2017. 149: p. 267-274. 68. Mueller, T., What is the thalamus in zebrafish? Frontiers in neuroscience, 2012. 6: p. 64. 69. Van Camp, N., et al., Light stimulus frequency dependence of activity in the rat visual system as studied with high-resolution BOLD fMRI. Journal of neurophysiology, 2006. 95(5): p. 3164-3170. 70. 方煒 and 饒瑞佶, 高亮度發光二極體在生物產業的應用. 中華農學會報, 5, 2004(5): p. 432-446. 71. Østergaard, F.G., et al., No detectable effect on visual responses using functional MRI in a rodent model of α-synuclein expression. Eneuro, 2021. 8(3). 72. Dinh, T.N.A., et al., Characteristics of fMRI responses to visual stimulation in anesthetized vs. awake mice. Neuroimage, 2021. 226: p. 117542. 73. Niranjan, A., et al., High-temporal-resolution BOLD responses to visual stimuli measured in the mouse superior colliculus. Matters, 2017. 3(2): p. e201701000001. 74. Lin, W., et al., 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. 39(3): p. 474-481. 75. Vovenko, E., 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. Pflügers Archiv, 1999. 437(4): p. 617-623. 76. McCormick, P.W., et al., Regional cerebrovascular oxygen saturation measured by optical spectroscopy in humans. Stroke, 1991. 22(5): p. 596-602. 77. Leenders, K., et al., Cerebral blood flow, blood volume and oxygen utilization: normal values and effect of age. Brain, 1990. 113(1): p. 27-47. 78. Fan, A.P., et al., Phase‐based regional oxygen metabolism (PROM) using MRI. Magnetic resonance in medicine, 2012. 67(3): p. 669-678. 79. Mandeville, J.B., et al., MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 1999. 42(5): p. 944-951. 80. Li, J., et al., Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping. Magnetic resonance in medicine, 2012. 68(5): p. 1563-1569. 81. 刘青松 and 邓成龙, 磁化率及其环境意义. 地球物理学报, 2009. 52(4): p. 1041-1048. 82. Hsieh, M.-C., 磁化率影像於大腦之研究與應用: 定量靜脈血氧濃度與鐵沉積. 臺灣大學生醫電子與資訊學研究所學位論文, 2016: p. 1-200. 83. Choi, M.-H., et al., Activation of the limbic system under 30% oxygen during a visuospatial task: An fMRI study. Neuroscience letters, 2010. 471(2): p. 70-73. 84. Lindauer, U., et al., Neuronal activity-induced changes of local cerebral microvascular blood oxygenation in the rat: effect of systemic hyperoxia or hypoxia. Brain research, 2003. 975(1-2): p. 135-140. 85. Chen, J.J. and G.B. Pike, Origins of the BOLD post-stimulus undershoot. Neuroimage, 2009. 46(3): p. 559-568. 86. Pawela, C.P., et al., A protocol for use of medetomidine anesthesia in rats for extended studies using task-induced BOLD contrast and resting-state functional connectivity. Neuroimage, 2009. 46(4): p. 1137-1147. 87. Wu, E.L., T.D. Chiueh, and J.H. Chen, Multiple‐frequency excitation wideband MRI (ME‐WMRI). Medical Physics, 2014. 41(9): p. 092304. 88. Lin, I.-T., H.-C. Yang, and J.-H. Chen, A temperature-stable cryo-system for high-temperature superconducting MR in-vivo imaging. PloS one, 2013. 8(4): p. e61958. 89. Smith, A.T., A.L. Williams, and K.D. Singh, Negative BOLD in the visual cortex: evidence against blood stealing. Human brain mapping, 2004. 21(4): p. 213-220. 90. Wei, H., et al., Joint 2D and 3D phase processing for quantitative susceptibility mapping: application to 2D echo‐planar imaging. NMR in Biomedicine, 2017. 30(4): p. e3501. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80383 | - |
| dc.description.abstract | 神經科學家已引入數種評估人類腦部活動的工具。其中，功能性磁振造影（fMRI）因其非侵入性和能將腦部功能對應到解剖結構等特性，成為最受研究者仰賴的工具之一。fMRI技術對大腦活化的血流動力學反應敏感，其取得之結果主要由幅度與相位資訊所組成。迄今，幅度資訊已被深入研究且成為血氧濃度相依（BOLD）fMRI的基礎，但相位資訊則相對地未被重視。本研究中，我們應用了新近發展的fMRI技術，即功能性定量磁化率影像（fQSM），基於相位資訊來評估大鼠腦部受光刺激之活化，並描述腦部的磁化率改變和血氧波動等等。 在光刺激實驗中，動物每0.2秒接受一次閃光以活化視覺路徑，其呼吸氣體包括低濃度(30%)或高濃度(100%)的氧氣，以突顯血氧波動與藉此評估靜脈血氧飽和度（SvO2）。透過fMRI，我們從相位資訊推導出fQSM結果，並與從幅度資訊推導出的BOLD結果進行比較。 從實驗結果，我們發現fQSM有多項特點：一、可行性高。fQSM可經由常用的fMRI程序來獲得。二、信號變化高。光刺激時，於兩種吸入氧氣濃度的條件下，fQSM的信號變化（從相位影像來的磁化率變化）皆是BOLD的信號變化（從幅度影像來的強度變化）的四倍。三、專一性高。從現象學上看，與 BOLD-fMRI產生之活化圖相比，fQSM 產生之活化圖其活化區域更多地局限於視覺路徑。四、非侵入性評估SvO2。透過應用fQSM，我們發現當大鼠呼吸低（高）濃度氧的氣體，在有光刺激時，校正後的SvO2約為84%（88%），而在無光刺激時，校正後的SvO2約為83%（87%）。五、我們還意外發現在峰值上，fQSM的反應比BOLD的反應慢。另外，我們發現fQSM的差異雜訊比(CNR)約是BOLD-fMRI的60%。 因此，與BOLD-fMRI相比， fQSM技術所提供之量化的磁化率資訊，其對外界刺激可展現明顯的反應，並突顯腦部區域的局部反應。此外，靜脈血的定量磁化率值還能更進一步直接轉化為SvO2，這是BOLD-fMRI無法提供的重要生理參數。此外，fQSM反應其峰值模式的代表意義，值得在不久的將來深入探討。就我們所知，本研究是第一個應用fQSM於光刺激的囓齒動物模型研究。我們的發現或可成為小動物fQSM應用的基礎框架。我們相信fQSM技術有潛力研究腦部疾病的機制並評估治療後的恢復情況，有朝一日將可能跟BODL fMRI一樣廣受重視。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:05:32Z (GMT). No. of bitstreams: 1 U0001-0305202110504600.pdf: 13868300 bytes, checksum: d8ffbd47254dd2318f0b1e386b1fb595 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 I 中文摘要 III ABSTRACT V CONTENTS VII LIST OF FIGURES X Chapter 1 Introduction 1 1.1 BOLD Contrast functional MRI (fMRI) 1 1.1.1 Discovery of BOLD Contrast 1 1.1.2 BOLD Signals and Hemodynamic Response 2 1.1.3 Features of BOLD-fMRI 4 1.2 Functional quantitative susceptibility mapping (QSM) 7 1.2.1 Features of QSM 7 1.2.2 Applications of QSM 10 1.2.3 Functional QSM (fQSM) and Its Applications 17 1.3 Motivation 20 Chapter 2 Material and Method 22 2.1 Animal preparation 22 2.2 Setup of Visual Stimulation System 25 2.3 MRI Acquisition 29 2.4 MR Data Processing 33 2.4.1 MR Image Reconstruction 33 2.4.2 Phase Unwrapping 36 2.4.3 Total Field Calculation 38 2.4.4 Background Field Removal 39 2.4.5 QSM Image Calculation 40 2.4.6 SvO2 Calculation 42 2.4.7 Statistical Analysis 42 Chapter 3 Results 43 3.1 The Raw Data and Corresponding Calculated Images 43 3.2 The Activation Map and Signal Time-Series 44 3.3 Visual Stimulus and Activation Areas 48 3.4 Scatter Diagram 49 3.5 Temporal evolution of SvO2 51 Chapter 4 Discussion 53 4.1 The Visual Stimulation 53 4.2 Partial Volume Effect (PVE) and Its Calibration 55 4.3 The Hemodynamic Response of fQSM 61 4.4 Analysis of BOLD-fMRI and fQSM mapping 63 4.5 Variations of fQSM 67 4.6 Effect of Field Strength and Temperature on 68 4.7 The Signal Changes and Noise in fQSM and BOLD-fMRI 69 4.8 Two Hyperoxic Conditions 71 4.9 Poststimulus Undershoot in BOLD Response 72 4.10 The Effect of Anesthesia 73 4.11 QSM Time Efficiency and Data Processing 74 4.12 Limitation 75 4.13 New Insights and Challenges We face 76 Chapter 5 Conclusion and Future Work 78 5.1 Conclusion 78 5.2 Future Work 79 Reference 82 Appendix 92 A.1 Demo package 92 B.1 Program operation for fQSM 100 C.1 Highlight of our present study 107 D.1 Journal paper 108 E.1 Conference paper 117 F.1 Functional evaluation of fQSM technology using 2D EPI sequence on the human brain 119 Publication List 126 | |
| dc.language.iso | en | |
| dc.subject | 靜脈血氧飽和度 | zh_TW |
| dc.subject | 定量磁化率圖 | zh_TW |
| dc.subject | 功能性MRI | zh_TW |
| dc.subject | 功能性定量磁化率圖 | zh_TW |
| dc.subject | 視覺刺激 | zh_TW |
| dc.subject | functional MRI | en |
| dc.subject | quantitative susceptibility mapping | en |
| dc.subject | venous oxygen saturation | en |
| dc.subject | visual stimuli | en |
| dc.subject | functional quantitative susceptibility mapping | en |
| dc.title | 功能性磁化率定量磁振影像於鼠腦視覺光刺激之研究 | zh_TW |
| dc.title | Functional Quantitative Susceptibility Mapping (fQSM) of Rat Brain during Flashing Light Stimulation | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 邱銘章(Hsin-Tsai Liu),成佳憲(Chih-Yang Tseng),廖漢文,林靜嫻,張允中,蘇家豪,林慶波 | |
| dc.subject.keyword | 定量磁化率圖,功能性MRI,功能性定量磁化率圖,視覺刺激,靜脈血氧飽和度, | zh_TW |
| dc.subject.keyword | quantitative susceptibility mapping,functional MRI,functional quantitative susceptibility mapping,visual stimuli,venous oxygen saturation, | en |
| dc.relation.page | 126 | |
| dc.identifier.doi | 10.6342/NTU202100870 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-09 | |
| 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 | |
|---|---|---|---|
| U0001-0305202110504600.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 13.54 MB | Adobe PDF |
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。