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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65006完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中明 | zh_TW |
| dc.contributor.author | 莊競程 | zh_TW |
| dc.contributor.author | Ching-Cheng Chuang | en |
| dc.date.accessioned | 2021-06-16T23:14:44Z | - |
| dc.date.available | 2023-11-10 | - |
| dc.date.copyright | 2012-08-10 | - |
| dc.date.issued | 2012 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | [1] F. F. Jöbsis, “Noninvasive infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science, vol. 198, pp. 1264-1267, 1977.
[2] Y. Hoshi, “Functional near-infrared spectroscopy: current status and future prospects,” J. Biomed. Opt., vol. 12, pp. 062106, 2007. [3] Y. Xu, N. Iftimia, H. Jiang, L. L. Key, and M. B. Bolster, “Threedimensional diffuse optical tomography of bones and joints,” J. Biomed. Opt., vol. 7, pp. 88-92, 2002. [4] D. R. Leff, O. J. Warren, L. C. Enfield, A. Gibson, T. Athanasiou, D. K. Patten, J. Hebden, G. Z. Yang, and A. Darzi, “Diffuse optical imaging of the healthy and diseased breast: A systematic review,” Breast Cancer Res. Treat, vol. 108, pp. 9-22, 2008. [5] A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage, vol. 49, pp. 561-567, 2009. [6] J. C. Hebden, A. Gibson, R. M. Yusof, N. Everdel, E. M. C. Hillman, D. T. Delpy, S. R. Arridge, T. Austin, J. H.Meek, and J. S.Wyatt, “Threedimensional optical tomography of the premature infant brain,” Phys. Med. Biol., vol. 47, pp. 4155-4166, 2002. [7] Y. Hoshi, M. Tamura, "Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man," Neurosci Lett, vol. 150, pp. 5-8, 1993. [8] T. Kato, A. Kamei, S. Takashima and T. Ozaki, "Human visual cortical function during photic stimulation monitoring by means of near-infrared spectroscopy," J. Cereb. Blood Flow Metab. vol. 13, pp. 516-520, 1993. [9] A. Villringer, J. Planck, C. Hock, L. Schleinkofer and U. Dirnag, "Near-infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults," Neurosci Lett. vol. 154, pp. 101-104, 1993. [10] R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli and G. Valentini, "Noninvasive absorption and scattering spectroscopy of bulk diffusive media: An application to the optical characterization of human breast," Applied Physics Letters, Vol. 74, pp. 874-876, 1999. [11] R. M. Herd, J. S. Dover and K. A. Arndt, "Basic laser principles," Dermatol Clin. vol. 15, pp. 355-372, 1997. [12] L. Rayleigh, "On the electromagnetic theory of light," Philos Mag., vol. 12, pp. 81-101, 1881. [13] G. Mie, "Beitrage zur Optik trüber Medien speziell kolloidaler Metallösungen," Ann Physik, vol. 25, pp. 377-445, 1908. [14] C. F. Bohren and D. R. Huffman, “Absorption and Scattering of Light by Small Particles,” John Wiley & Sons, 1983. [15] A. Pellicer and M. del C. Bravo, "Near-infrared spectroscopy: A methodology-focused review," Semin. Fetal. Neonat. M., Vol. 16, pp. 42-49, 2011. [16] L. V. Wang and H. I. Wu, “Biomedical Optics,” Wiley, 2007. (ISBN 978-0-471-74304-0) [17] M. Firbank, S. R. Arridge, M. Schweiger and D. T. Delpy, “An investigation of light transport through scattering bodies with non-scattering regions,” Phys. Med. Biol., vol. 41, pp. 767-783, 1996. [18] C. K. Lee, C. W. Sun, P. L. Lee, H. C. Lee, C. C. Yang, C. P. Jiang, Y. P. Tong, T. C. Yeh and J. C. Hsieh, “Study of photon migration with various sourcedetector separations in near-infrared spectroscopic brain imaging based on three-dimensional Monte Carlo modeling,” Opt. Express, vol. 13, pp. 8339-8348, 2005. [19] L. H. Wang, S. L. Jacques and L. –Q. Zheng, “MCML – Monte Carlo modeling of photon transport in multilayered tissues,” Comput. Meth. Prog. Bio., vol. 47, pp. 131-146, 1995. [20] M. Kirillin, I. Meglinski, V. Kuzmin, E. Sergeeva and R. Myllylä, “Simulation of optical coherence tomography images by Monte Carlo modeling based on polarization vector approach,” Opt. Express, vol. 18, pp.21714-21724, 2010. [21] D. Y. Churmakov, I. V. Meglinski and D. A. Greenhalgh, “Influence of refractive index matching on the photon diffuse reflectance,” Phys. Med. Biol., vol. 47, pp. 4271-4285, 2002. [22] I. V. Meglinsky and S. J. Matcher, “Modelling the sampling volume for skin blood oxygenation measurements,” Med. Biol. Eng. Comput., vol. 39, pp. 44-50, 2001. [23] C. Mansouri, J. P. L. Huillier, N. H. Kashou and A. Humeau, “Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging,” Lasers Med. Sci., vol. 25, pp. 431-438, 2010. [24] D. A. Boas, J. P. Culver, J. J. Stott and A. K. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Optics Express., vol. 10, pp. 159-170, 2001. [25] D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. A. Marota and J. B. Mandeville, “The Accuracy of Near Infrared Spectroscopy and Imaging during Focal Changes in Cerebral Hemodynamics,” NeuroImage, vol. 13, pp. 76-90, 2001. [26] C. -Y. Wang, M. -L. Chuang, S. -J. Liang, J. -C. Tsai, C. -C. Chuang, Y. -S. Hsieh, C. -W. Lu, P. -L. Lee, and C. -W. Sun, “Diffuse Optical Multipatch Technique for Tissue Oxygenation Monitoring: Clinical Study in Intensive Care Unit,” IEEE. Trans. Biomed. Eng., vol. 59, pp. 87-94, 2012. [27] G. Strangman, D. A. Boas, J. P. Sutton, “Non-invasive neuroimaging using near-infrared light,” Biol. Psychiatry, vol. 52, pp. 679-693, 2002. [28] M. Wolf, U. Wolf, J. H. Choi, R. Gupta, L. P. Safonova, L. A. Paunescu, A. Michalos, E. Gratton, “Functional Frequency-Domain Near-Infrared Spectroscopy Detects Fast Neuronal Signal in the Motor Cortex,” NeuroImage, vol. 17, pp. 1868-1875, 2002. [29] G. Gratton, C. R. Brumback, B. A. Gordon, M. A. Pearson, K. A. Low, M. Fabiani, “Effects of measurement method, wavelength, and source-detector distance on the fast optical signal,” NeuroImage, vol. 32, pp. 1576-1590, 2006. [30] A. V. Medvedeva, J. Kainerstorferb, S. V. Borisova, R. L. Barbourc, J. VanMetera, “Event-related fast optical signal in a rapid object recognition task: Improving detection by the independent component analysis,” Brain. Res, vol. 1236, pp. 145-158, 2008. [31] E. M. C. Hillman, “Optical brain imaging in vivo: techniques and applications from animal to man,” J. Biomed. Opt., vol. 12, pp. 051402, 2007. [32] H. Obrig, R. Wenzel, M. Kohl, S. Horst, P. Wobst, J. Steinbrink, F. Thomas, A. Villringer, “Near-infrared spectroscopy: does it function in functional activation studies of the adult brain?” J. Psychophysiol, vol. 35, pp. 125-142, 2000. [33] D. A. Boas, T. Gaudette, G. Strangman, X. Cheng, J. J. A. Marota, J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage, vol. 13, pp. 76-90, 2001. [34] S. C. Gebhart, W. C. Lin, A. M. Jansen, “In vitro determination of normal and neoplastic human brain tissue optical properties using inverse adding-doubling,” Phys. Med. Biol., vol. 51, pp. 2011-2027, 2006. [35] H. Zhao, Y. Tanikawa, F. Gao, Y. Onodera, A. Sassaroli, K. Tanaka, Y. Yamada, “Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR,” Phys. Med. Biol., vol. 47, pp. 2075-2093, 2002. [36] F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, C. Depeursinge, “In vivo local determination of tissue optical properties: applications to human brain,” Appl. Optics., vol. 38, pp. 4939-4950, 1999. [37] T. Li, H. Gong, Q. Luo, “Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy,” J. Biomed. Opt., vol. 16, pp. (045001) 1-6, 2011. [38] E. Okada, D. T. Delpy, “Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer,” Appl. Optics, vol. 42, pp. 2906-2914, 2003. [39] Y. Fukui, Y. Ajichi, E. Okada, “Monte Carlo prediction of near-infrared light propagation in realistic adult and neonatal head models,” Appl. Optics, vol. 42, pp. 2881-2887, 2003. [40] R. Mudra, A. Nadler, E. Keller, P. Niederer, “Analysis of near-infrared spectroscopy and indocyanine green dye dilution with Monte Carlo simulation of light propagation in the adult brain,” J. Biomed. Opt., vol. 11, pp. 044009, 2006. [41] Y. Xu, H. L. Graber, R. L. Barbour, “Image correction algorithm for functional three-dimensional diffuse optical tomography brain imaging,” Appl. Optics, vol. 46, pp. 1693-1704, 2007. [42] E. Okada, D. T. Delpy, “Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal,” Appl. Optics, vol. 42, pp. 2915-2922, 2003. [43] D. A. Boas, A. M. Dale, “Simulation study of magnetic resonance imaging–guided cortically constrained diffuse optical tomography of human brain function,” Appl. Optics, vol. 44, pp. 1957-1968, 2005. [44] Y. Ogoshi, E. Okada, “Analysis of light propagation in a realistic head model by a hybrid method for optical brain function measurement,” Opt. Rev., vol. 12, pp. 264-269, 2005. [45] J. Heiskala, I. Nissilä, T. Neuvonen, S. Järvenpää, E. Somersalo, “Modeling anisotropic light propagation in a realistic model of the human head,” Appl. Optics, vol. 44, pp. 2049-2057, 2005. [46] T. Hayashi, Y. O. Kashio, E. Okada, “Hybrid Monte Carlo–diffusion method for light propagation in tissue with a low-scattering region,” Appl. Optics, vol. 42, pp. 2888-2896, 2003. [47] Y. Hoshi, M. Shimada, C. Sato, Y. Iguchi, “Reevaluation of near-infrared light propagation in the adult human head: implications for functional near-infrared spectroscopy,” J. Biomed. Opt., vol. 10, pp. 064032, 2005. [48] S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, D. A. Boas, “Dynamic physiological modeling for functional diffuse optical tomography,” NeuroImage, vol. 30, pp. 88-101, 2006. [49] M. Firbanky, S. R. Arridgez, M. Schweigery, D. T. Delpy, “An investigation of light transport through scattering bodies with non-scattering regions,” Phys. Med. Biol., vol. 41, pp. 767-783, 1996. [50] M. Wolf, M. Keel, V. Dietz, K. von Siebenthal, H. U. Bucher, O. Baenziger, “The influence of a clear layer on near-infrared spectrophotometry measurements using a liquid neonatal head phantom,” Phys. Med. Biol., vol. 44, pp. 1743-1753, 1999. [51] H. Dehghani, D. T. Delpy, S. R. Arridge, “Photon migration in non-scattering tissue and the effects on image reconstruction,” Phys. Med. Biol., vol. 44, pp. 2897-2906, 1999. [52] S. Oshe, “Fronts Propagating with Curvature- Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations,” J. Comput. Phys., vol. 79, pp. 12-49, 1998. [53] T. R. Raviv, N. Kiryati and N. Sochen, “Segmentation by Level Sets and Symmetry,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Volume 1 CVPR06., 00, pp. 1015-1022, 2006. [54] M. E. Leventon, O. Faugeras, W. E. L. Grimson, W. M. Wells, “Level Set Based Segmentation with Intensity and Curvature Priors,” IEEE Workshop on Math Met Biomed Imag Anal, vol. 00, pp. 4-11, 2000. [55] C. Li, C. Xu, C. Gui, M. D. Fox, “Level set evolution without re-initialization: a new variational formulation,” IEEE Comp. Soc. Conf. Comp. Vis. Pattern Recogn., vol. 1, pp. 430-436, 2005. [56] R. B. Dubey, M. Hanmandlu, and S. K. Gupta, “Level set detected masses in digital mammograms,” Indian J. Sci. Technol., vol. 3, pp. 9-13, 2010. [57] S. A. Hojjatoleslami, J. Kittler, “Region growing: a new approach,” IEEE T. Image Process, vol. 7, pp. 1079-1084, 1998. [58] A. Tremeau, N. Borel, “A region growing and merging algorithm to color segmentation,” Pattern Recognition, vol. 30, pp. 1191-1203, 1997. [59] J. Ashburner, K. J. Friston, “Unified segmentation,” NeuroImage, vol. 26, pp. 839-851, 2005. [60] I. V. Meglinsky, S. J. Matcher, “Modelling the sampling volume for skin blood oxygenation measurements,” Med. Biol. Eng. Comput., vol. 39, pp. 44-50, 2001. [61] C. Mansouri, J. P. L. Huillier, N. H. Kashou, A. Humeau, “Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging,” Lasers Med. Sci., vol. 25, pp. 431-438, 2010. [62] D. A. Boas, J. P. Culver, J. J. Stott, A. K. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Opt. Express, vol. 10, pp. 159-170, 2001. [63] C. -K. Lee, C. -W. Sun, P. -L. Lee, H. -C. Lee, C. -C. Yang, C. -P. Jiang, Y. -P. Tong, T. -C. Yeh, and J. -C. Hsieh, “Study of photon migration with various sourcedetector separations in near-infrared spectroscopic brain imaging based on threedimensional Monte Carlo modeling,” Opt. Express, vol. 13, pp. 8339-8348 2005. [64] L. AnAn, L. Qian, Z. ShaoQun, T. Lei, Z. ShiZhen, and L. QingMing, “Construction and visualization of high-resolution three-dimensional anatomical structure datasets for Chinese digital human,” Chinese Science Bulletin, vol. 53, pp. 1848-1854, 2008. [65] C. -Y. Lo, P. -N. Wang, K. -H. Chou, J. Wang, Y. He, and C. -P. Lin, “Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease,” J. Neurosci., vol. 30, pp. 16876-85, 2010. [66] S. -X. Zhang, P. -A. Heng, Z. -J. Liu, L. -W. Tan, M. -G. Qiu, Q. -Y. Li, R. -X. Liao, K. Li, G. -Y. Cui, Y. -L. Guo, X. -P. Yang, G. -J. Liu, J. -L. Shan, J. -J. Liu, W. -G. Zhang, X. -H. Chen, J. -H. Chen, J. Wang, W. Chen, M. Lu, J. You, X. -L. Pang, H. Xiao, Y. -M. Xie, and J. C. -Y. Cheng, “The Chinese Visible Human (CVH) datasets incorporate technical and imaging advances on earlier digital humans,” J. Anat., vol. 204, pp. 165-173, 2004. [67] H. Koizumi, T.i Yamamoto, A. Maki, Y. Yamashita, H. Sato, H. Kawaguchi, and N. Ichikawa, “Optical topography: practical problems and new applications,” Appl. Opt., vol. 42, pp. 3054-3062, 2003. [68] D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Optics Letters, vol. 29, pp. 1506-1508, 2004. [69] R. I. Scahill, C. Frost, R. Jenkins, J. L. Whitwell, M. N. Rossor, and N. C. Fox, “A Longitudinal Study of Brain Volume Changes in Normal Aging Using Serial Registered Magnetic Resonance Imaging,” Arch. Neurol., vol. 60, pp. 989-994, 2003. [70] S. M. Resnick, A. F. Goldszal, C. Davatzikos, S. Golski, M. A. Kraut, E. J. Metter, R. N.Bryan, and A. B. Zonderman, “One-year Age Changes in MRI Brain Volumes in Older Adults,” Cereb. Cortex., vol. 10, pp. 464-472, 2000. [71] C. E. Coffey, W. E. Wilkinson, L. A. Parashos, S. A. R. Soady, R. J. Sullivan, L. J. Patterson, G. S. Figiel, M. C. Webb, C. E. Spritzer, and W. T. Djang, “Quantitative cerebral anatomy of the aging human brain A cross‐sectional study using magnetic resonance imaging,” Neurology, vol. 42, pp. 527-536, 1992. [72] D. T. Chard, C. M. Griffn,G. J. M. Parker, R. Kapoor, A. J. Thompson and D. H. Miller, “Brain atrophy in clinically early relapsing-remitting multiple sclerosis,” Brain, vol.125, pp. 327-337, 2002. [73] M. Pievani, F. Agosta, E. Pagani, E. Canu, S. Sala, M. Absinta, C. Geroldi, R. Ganzola, G. B. Frisoni, and M. Filippi, “Assessment of white matter tract damage in mild cognitive impairment and Alzheimer’s disease,” Human Brain Mapping, vol. 31, pp. 1862-1875, 2010. [74] G. Chételat, B. Landeau, F. Eustache, F. Mézenge, F. Viader, V. de la Sayette, B. Desgranges, and J. C. Baron, “Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study,” NeuroImage, vol. 27, pp. 934-946, 2005. [75] L. E. DeLisi, “The Concept of Progressive Brain Change in Schizophrenia: Implications for Understanding Schizophrenia,” Schizophrenia Bulletin, vol. 34, pp. 312-321, 2008. [76] J. M. Bjork, S. J. Grant, D. W. Hommer, “Cross-sectional volumetric analysis of brain atrophy in alcohol dependence: Effects of drinking history and comorbid substance use disorder,” Am. J. Psychiatry, vol.160, pp. 2038-2045, 2003. [77] M. Boccardi, M. P. Laakso, L. Bresciani, S. Galluzzi, C. Geroldi, A. Beltramello, H. Soininen and G. B. Frisoni, “The MRI pattern of frontal and temporal brain atrophy in fronto-temporal dementia,” Neurobiology of Aging, vol. 24, pp. 95-103, 2003. [78] Q. Mu, J. Xie, Z. Wen, Y. Weng and Z. Shuyun, “A quantitative MR study of the hippocampal formation, the amygdala, and the temporal horn of the lateral ventricle in healthy subjects 40 to 90 years of age,” AJNR, vol. 20, pp. 207-211, 1999. [79] P. E. Cowell, B. I. Turetsky, R. C. Gur, R. I. Grossman, D. L. Shtasel and R. E. Gurl, “Sex differences in aging of the human frontal and temporal lobes,” J. Neurosci., vol. 14, pp. 4749-4755, 1994. [80] C. E. Coffey, J.A. Saxton, G. Ratcliff, R.N. Bryan and J.F. Lucke, “Relation of education to brain size in normal aging: Implications for the reserve hypothesis,” Neurology, vol. 53, pp. 189-196, 1999. [81] G. L. Wenk, “Neuropathologic changes in Alzheimer's disease,” J. Clin. Psychiatry, vol. 64, pp. 7-10, 2003. [82] X. Delbeuck, M. V. der Linden, and F. Collette, “Alzheimer’s disease as a disconnection syndrome ?,” Neuropsychology Review, vol. 13, pp. 79-92, 2003. [83] H. J. Rosen, M. L. Gorno–Tempini, W. P. Goldman, R. J. Perry, N. Schuff, M. Weiner, R. Feiwell, J. H. Kramer and B. L. Miller, “Patterns of brain atrophy in frontotemporal dementia and semantic dementia,” Neurology, vol. 58, pp. 198-208, 2002. [84] Y. Ge, R. I. Grossman, J. S Babb, M. L Rabin, L. J. Mannon, and D. L. Kolson, “Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis,” AJNR. Am. J. Neuroradiol., vol. 23, pp. 1327-33, 2002. [85] J. L. Whitwell, S. A. Przybelski, S. D. Weigand, D. S. Knopman, B. F. Boeve, R. C. Petersen, and C. R. Jack Jr, “3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease,” Brain, vol. 130, pp. 1777-1786, 2007. [86] C. D. Smith, H. Chebrolu, D. R. Wekstein, F. A. Schmitt, G. A. Jicha, G. Cooper, and W.R. Markesbery, “Brain structural alterations before mild cognitive impairment,” Neurology, vol. 68, pp. 1268-1273, 2007. [87] K. Sakatani, Y. Katayama, T. Yamamoto, and S. Suzuki, “Changes in cerebral blood oxygenation of the frontal lobe induced by direct electrical stimulation of thalamus and globus pallidus: a near infrared spectroscopy study,” J. Neurol Neurosurg Psychiatry, vol. 67, pp. 769-773, 1999. [88] T. Suto, M. Fukuda, M. Ito, T. Uehara, and M. Mikuni, ”Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study,” BIOL. Psychiatry, vol. 55, pp. 501-511, 2004. [89] M. J. Herrmann, A. C. Ehlis, A. Wagener, C. P. Jacob, and A. J. Fallgatter, “Near-infrared optical topography to assess activation of the parietal cortex during a visuo-spatial task,” Neuropsychologia, vol. 43, pp. 1713-1720, 2005. [90] H. Arai, M. Takano, K. Miyakawa, T. Ota, T. Takahashi, H. Asaka, and T. Kawaguchi, “A quantitative near-infrared spectroscopy study: A decrease in cerebral hemoglobin oxygenation in Alzheimer’s disease and mild cognitive impairment,” Brain and Cognition, vol. 61, pp. 189-194, 2006. [91] Y. Fan, N. Batmanghelich, C. M. Clark and C. Davatzikosa, “Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline,” NeuroImage, vol. 39, pp. 1731-1743, 2008. [92] B. C. Dickerson, T. R. Stoub, R. C. Shah, R. A. Sperling, R. J. Killiany, M. S. Albert, B. T. Hyman, D. Blacker and L. de T. -Morrell, “Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults,” Neurology, vol. 76, pp. 1395-1402, 2011. [93] S. A. Thompson, K. Patterson and J. R. Hodges, “Left/right asymmetry of atrophy in semantic dementia: Behavioral-cognitive implications,” Neurology, vol. 61, pp. 1196-1203, 2003. [94] K. Chen, E. M. Reiman, G. E. Alexander, D. Bandy, R. Renaut, W. R. Crum, N. C. Fox and M. N. Rossor, “An automated algorithm for the computation of brain volume change from sequential MRIs using an iterative principal component analysis and its evaluation for the assessment of whole-brain atrophy rates in patients with probable Alzheimer’s disease,” NeuroImage, vol. 22, pp. 134-143, 2004. [95] J. Snowden, D. Neary and D. Mann, “Frontotemporal lobar degeneration: clinical and pathological relationships,” Acta Neuropathol, vol. 114, pp. 31-38, 2007. [96] S. Burgmansa, M.P.J. van Boxtel, F. Smeets , E.F.P.M. Vuurman, E.H.B.M. Gronenschild, F.R.J. Verhey, H.B.M. Uylings, J. Jolles, “Prefrontal cortex atrophy predicts dementia over a six-year period,” Neurobiology of Aging, vol. 30, pp. 1413-1419, 2009. [97] D. H. Salat, J. A. Kaye, J. S. Janowsky, “Prefrontal Gray and White Matter Volumes in Healthy Aging and Alzheimer Disease,” Arch Neurol., vol. 56, pp. 338-344, 1999. [98] G. Strangman, D. A. Boas, and J. P. Sutton, "Non-invasive neuroimaging using near-infrared light," Biol. Psychiatry., vol. 52, pp. 679-693, 2002. [99] M. Wolf, U. Wolf, V. Toronov, A. Michalos, L. A. Paunescu, J. H. Choi and E. Gratton, “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: A near-infrared spectroscopy study,” NeuroImage, Vol. 16, pp. 704-712, 2002. [100] G. Boas, "Noninvasive Imaging of the Brain," Optics News, vol. 15, pp. 52-55, 2004. [101] T. Walsh, J. M. McClellan, S. E. McCarthy, A. M. Addington, S. B. Pierce, G. M. Cooper, A. S. Nord, M. Kusenda, D. Malhotra, A. Bhandari, S. M. Stray, C. F. Rippey, P. Roccanova, V. Makarov, B. Lakshmi, R. L. Findling, L. Sikich, T. Stromberg, B. Merriman, N. Gogtay, P. Butler, K. Eckstrand, L. Noory, P. Gochman, R. Long, Z. Chen, S. Davis, C. Baker, E. E. Eichler, P. S. Meltzer, S. F. Nelson, A. B. Singleton, M. K. Lee, J. L. Rapoport, M. -C. King and J. Sebat, “Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia,” Science vol.320, pp. 539-543, 2008. [102] R. I. M. Dunbar, “The social brain hypothesis,” Evol. Anthropol vol. 6, pp. 178-190, 1998. [103] G. Bush, P. Luu and M. I. Posner, “Cognitive and emotional influences in anterior cingulate cortex,” Trends Cogn. Sci. vol. 4, pp. 215-222, 2000. [104] T. Jellema, C. I. Baker, B. Wicker and D. I. Perrett, “Neural representation for the perception of the intentionality of actions,” Brain Cognition vol. 44, pp. 280-302, 2000. [105] J. K. Burns, “An evolutionary theory of schizophrenia: Cortical connectivity, metarepresentation, and the social brain,” Behav. Brain Sci. vol. 27, pp. 831-885, 2004. [106] D. S. Manoach, D. Z. Press, V. Thangaraj, M. M. Searl, D. C. Goff, E. Halpern, C. B. Saper and S. Warach, “Schizophrenic subjects activate dorsolateral prefrontal cortex during a working memory task, as measured by fMRI,” Biol. Psychiatry vol. 45, pp. 1128-1137, 1999. [107] R. E. Gur, C. McGrath, R. M. Chan, L. Schroeder, T. Turner, B. I. Turetsky, C. Kohler, D. Alsop, J. Maldjian, J. D. Ragland, and R. C. Gur, “An fMRI study of facial emotion processing in patients with schizophrenia,” Am. J. Psychiatry vol. 159, pp.1992-1999, 2002. [108] Y. Zhou, M. Liang, L. Tian, K. Wang, Y. Hao, H. Liu, Z. Liu and T. Jiang, “Functional disintegration in paranoid schizophrenia using resting-state fMRI,” Schizophr. Res. vol. 97, pp. 194-205, 2007. [109] Y. Zhoua, M. Liang, T. Jiang, L. Tian, Y. Liu, Z. Liu, H. Liu and F. Kuang, “Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI,” Neurosci. Lett. vol. 417, pp. 297-302, 2007. [110] D. Mamo, A. Graff, R. Mizrahi, C. M. Shammi, F. Romeyer and S. Kapur, “Differential effects of aripiprazole on D2, 5-HT2, and 5-HT1A receptor occupancy in patients with schizophrenia: a triple tracer PET study,” Am. J. Psychiatry vol. 164, pp. 1411-1417, 2007. [111] R. Mizrahi, P. Rusjan, O. Agid, A. Graff, D. C. Mamo, R. B. Zipursky and Shitij Kapur, “Adverse subjective experience with antipsychotics and its relationship to striatal and extrastriatal D2 receptors: a PET study in schizophrenia,” Am. J. Psychiatry vol. 164, pp. 630–637, 2007. [112] O. D. Howes, A. J. Montgomery, M. -C. Asselin, R. M. Murray, I. Valli, P. Tabraham, E. B. -Bosch, L. Valmaggia, L. Johns, M. Broome, P. K. McGuire and P. M. Grasby, “Elevated striatal dopamine function linked to prodromal signs of schizophrenia,” Arch. Gen. Psychiatry. vol. 66, pp. 13-20, 2009. [113] G. Boas, “Noninvasive imaging of the brain,” Optics & Photonics News vol. 15, pp. 52-55, 2004. [114] S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral and K. Pourrezaei, “Functional near-infrared spectroscopy,” IEEE Eng. Med. Biol. vol. 25, pp. 54-62, 2006. [115] Y. Fukuji, T. Bise, H. Hokama, K. Hiramatsu and C. Ogura, “Changes of cerebral blood volume in frontal region during mirror drawing task measured by two channel NIRS in schizophrenics,” International Congress Series vol. 1232, pp. 667-671, 2002. [116] B. S. Folley and S. Park, “Verbal creativity and schizotypal personality in relation to prefrontal hemispheric laterality: A behavioral and near-infrared optical imaging study,” Schizophr. Res. vol. 80, pp. 271-282, 2005. [117] S. Sumitani, T. Tanaka, S. Y. Tayoshi, K. Ota, N. Kameoka, S.-i. Ueno and T. Ohmori, “Activation of the prefrontal cortex during the wisconsin card sorting test as measured by multichannel near-infrared spectroscopy,” Neuropsychobiology vol. 53, pp. 70-76, 2006. [118] Y. Kubotaa, M. Toichib, M. Shimizua, R. A. Masona, C. M. Coconceac, R. L. Findlingb, K. Yamamotod and J. R. Calabrese, “Prefrontal activation during verbal fluency tests in schizophrenia-a near-infrared spectroscopy (NIRS) study,” Schizophr. Res. vol. 77, pp. 65-73, 2005. [119] A. -C. Ehlis, M. J. Herrmann, M. M. Plichta and A. J. Fallgatter, “Cortical activation during two verbal fluency tasks in schizophrenic patients and healthy controls as assessed by multi-channel near-infrared spectroscopy,” Psychiat Res-Neuroim. vol. 156, pp. 1-13, 2007. [120] R. Takizawa, K. Kasai, Y. Kawakubo, K. Marumo, S. Kawasaki, H. Yamasue and M. Fukuda, “Reduced frontopolar activation during verbal fluency task in schizophrenia: A multi-channel near-infrared spectroscopy study,” Schizophr. Res. vol. 99, pp. 250-262, 2008. [121] H. Hori, M. Nagamine, T. Soshi, S, Okabe, Y. Kim and H. Kunugi, “Schizotypal traits in healthy women predict prefrontal activation patterns during a verbal fluency task: a near-infrared spectroscopy study,” Neuropsychobiology vol. 57, pp. 61-69, 2008. [122] K. Ikezawa, M. Iwase, R. Ishii, M. Azechi, L. Canuet, K. Ohi, Y. Yasuda, N. Iike, R. Kurimoto, H. Takahashi, T. Nakahachi, R. Sekiyama, T. Yoshida, H. Kazui, R. Hashimoto and M. Takeda, “Impaired regional hemodynamic response in schizophrenia during multiple prefrontal activation tasks: A two-channel near-infrared spectroscopy study,” Schizophr. Res. vol. 108, pp. 93-103, 2009. [123] M. First, R. Spitzer, M. Gibbon and J. B. Williams, "Structured clinical interview for DSM‐IV‐TR axis I disorders, research version, patient edition. (SCID‐I/P)" Biometrics research, New York State Psychiatric Institute, New York, 2002. [124] S. R. Kay, A. Fiszbein and L. A. Opler, "The positive and negative syndrome scale (PANSS) for schizophrenia," Schizophr. Bull. vol. 13, pp. 261-275, 1987. [125] S. Leuchta, J. M. Kane, W. Kissling, J. Hamann, E. Etschel and R. R. Engel, “What does the PANSS mean?,” Schizophr. Res. vol. 79, pp. 231-238, 2005. [126] S. H. Jones, G. Thornicroft, M. Coffey and G. Dunn, “A brief mental health outcome scale reliability and validity of the Global Assessment of Functioning (GAF),” Brit. J. Psychiat. vol. 166, pp. 654-659, 1995. [127] M. Startup, M. C. Jackson and S. Bendix, “The concurrent validity of the Global Assessment of Functioning (GAF),” British Journal of Clinical Ps. vol. 41, pp. 417-422, 2002. [128] Y. Yamashita, A. Maki, Y. Ito, E. Watanabe, Y. Mayanagi and H. Koizumi, “Noninvasive near-infrared topography of human brain activity using intensity modulation spectroscopy,” Opt. Eng. vol. 35, pp. 1046-1049, 1996. [129] L. Kocsis, P. Herman and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol. vol. 51, pp. N91-N98, 2006. [130] Y. Hoshi, “Functional near-infrared spectroscopy: current status and future prospects,” J. Biomed. Opt. vol. 12, pp. 062106 1-9, 2007. [131] C. -M. Lu, Y. -J. Zhang, B. B. Biswal, Y. -F. Zang, D. -L. Peng and C. -Z. Zhu, “Use of fNIRS to assess resting state functional connectivity,” J. Neurosci. Meth. vol. 186, pp. 242-249, 2010. [132] M. Okamoto, H. Dan, K. Sakamoto, K. Takeo, K. Shimizu, S. Kohno, I. Oda, S. Isobe, T. Suzuki, K. Kohyama and I. Dan, “Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping,” NeuroImage vol. 21, pp. 99-111, 2004. [133] R. L. Plackett, “Karl pearson and the chi-squared test,” Int. Stat. Rev. vol. 51, pp. 59-72, 1983. [134] D. M. Glover, W. J. Jenkins and S. C. Doney, “Least Squares and regression techniques, goodness of fit and tests, non-linear least squares techniques,” Woods Hole Oceanographic Institute, 2008. [135] R. H.C. Lopes, I. Reid and P. R. Hobson, “The two-dimensional Kolmogorov-Smirnov test,” Proceedings of science, pp. 1-12, 2007. [136] G. W. Corder and D. I. Foreman, “Nonparametric Statistics for Non‐Statisticians: A Step‐by‐Step Approach,” Wiley, 2009. [137] D. Arthur, B. Manthey and H. Roeglin, “k‐means has polynomial smoothed complexity,” Proceedings of the 50th Symposium on Foundations of Computer Science (FOCS), 2009. [138] H. Zha, C. Ding, M. Gu, X. He and H.D. Simon, “Spectral Relaxation for K‐means Clustering,” NIPS vol. 14, pp. 1057-1064, 2001. [139] M. Azechi, M. Iwase, K. Ikezawa, H. Takahashi, L. Canuet, R. Kurimoto, T. Nakahachi, R. Ishii, M. Fukumoto, K. Ohi, Y. Yasuda, H. Kazui, R. Hashimoto and M. Takeda, “Discriminant analysis in schizophrenia and healthy subjects using prefrontal activation during frontal lobe tasks: A near-infrared spectroscopy,” Schizophr. Res. vol. 117, pp. 52-60, 2010. [140] Y. Kubotaa, M. Toichib, M. Shimizua, R. A. Masona, C. M. Coconcea, R. L. Findling, K. Yamamoto and J. R. Calabrese, “Prefrontal activation during verbal fluency tests in schizophrenia—a near-infrared spectroscopy (NIRS) study,” Schizophr. Res. vol. 77, pp. 65-73, 2005. [141] M. J. Herrmann, A. Walter, A. -C. Ehlis and A. J. Fallgatter, “Cerebral oxygenation changes in the prefrontal cortex: Effects of age and gender,” Neurobiol. Aging vol. 27, pp. 888-894, 2006. [142] D. A. Yurgelun-Todd, C. M. Waternaux, B. M. Cohen, S. A. Gruber, C. D. English and P. F. Renshaw, “Functional magnetic resonance imaging of schizophrenic patients and comparison subjects during word production,” Am. J. Psychiatry vol. 153, pp. 200-205, 1996. [143] P. G. Nestor, S. D. Han, M. Niznikiewicz, D. Salisbury, K. Spencer, M. E. Shenton and R. W. McCarley, “Semantic disturbance in schizophrenia and its relationship to the cognitive neuroscience of attention,” Biol. Psychol. vol. 57, pp. 23-46, 2001. [144] A. J. Fallgatter, W. K. Strik, “Reduced frontal functional asymmetry in schizophrenia during a cued continuous performance test assessed with near-infrared spectroscopy,” Schizophr Bull., vol. 26, pp. 913-919, 2000. [145] J. Lee, Bradley S. Folley, J. Gore, and S. Park, “Origins of spatial working memory deficits in schizophrenia: an event-related FMRI and near-infrared spectroscopy study,” PLoS. One., vol. 3, pp. e1760., 2008. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65006 | - |
| dc.description.abstract | 近紅外擴散光學造影是一項非侵入式技術，廣泛地做為量測組織生理的研究工具，特別是在大腦造影方面。各種光學造影技術已經被開發，但仍然幾項問題存在，包含訊號雜訊比的評估、複雜的影像重建演算法之簡化、光源與檢測器間距之最佳化選擇、大腦結構對光子遷徙的影響、以及大腦體積造影，都仍待充分地瞭解。在本論文中，我們首先透過蒙地卡羅的模擬來研究大腦結構影像對於近紅外擴散光學造影技術的影響。另外，在大腦功能性造影方面，我們展示了功能性近紅外造影技術在精神分裂症鑑別分析之臨床應用。
在大腦結構性造影方面，我們以體內MRI T1影像透過影像處理技術重建的三維大腦模型，來探討近紅外光量測的個體化校正，並藉由蒙地卡羅進行模擬分析。透過蒙地卡羅模擬，光子在基於三維MRI影像所重建的大腦模型中之傳播已經被報告；然而，並沒有以病人導向的模擬來進行近紅外光學量測的個體化校正。我們的結果顯示，透過三維MRI時間解析的大腦結構重建模擬方法以接近真實的人腦情況，來對於近紅外光學系統設計和具有先驗MRI數據的個體化校正提供有用的訊息。探討以近紅外光量測腦部結構對於空間敏感度分布之個體差異，成年與老年志願者兩種大腦模型被建立，並藉由不同光源與檢測器的間距來進行蒙地卡羅模擬。結果指出，近紅外光造影技術在大腦萎縮而產生的結構性訊號的檢測上，是一項可行且靈敏的方法。進一步地，我們提出一項嶄新的近紅外光大腦體積造影方法，用以量測大腦體積結構的變化。使用大腦體積變化的不同特徵來建構正常人、老年人、和典型的阿茲海默症患者的模型，並透過蒙地卡羅模擬來研究光子在此三種不同大腦結構中的相關特性。我們的研究顯示近紅外光大腦體積造影技術能夠在神經退化性疾病之臨床應用上指出大腦萎縮程度，並且以病人導向的方式進行量測。 對於精神分裂症的鑑別分析，我們也展示功能性近紅外光造影技術之臨床應用。透過語言流暢度測試，我們對於大腦前額葉皮質的功能性光學斷層掃描造影進行統計分析，在精神分裂症與正常對照之間的影像結果是顯著差異的，特別是在左前額葉皮質。根據我們的結果，近紅外擴散光學造影技術基於其本身的優點，可以成為在病患導向診斷上的有用臨床與研究工具。 | zh_TW |
| dc.description.abstract | Near-infrared diffuse optical imaging is a non-invasive technique comprehensive as a research tool to measure tissue physiology, particularly in brain imaging. Various optical imaging techniques have been developed, but several issues remain. These, including signal-to-noise ratio evaluation, simplifying the complex algorithms of image reconstruction, the optimal choice of source-detector separation, the brain structural effects on light propagation, and the brain volume imaging, remain to be fully understood. In this thesis, we first investigate the near-infrared diffuse optical imaging technique for brain structural imaging with Monte Carlo simulation. Besides, for brain functional imaging, we demonstrate functional near-infrared spectroscopy (fNIRS) measurements on the clinical application for discriminant analysis of schizophrenia diagnosis.
For brain structural imaging, we offer an approach for brain modeling based on the image segmentation process with in vivo magnetic resonance imaging (MRI) T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation. Monte Carlo simulations of light propagation in full-segmented three-dimensional MRI-based anatomical models of the human head have been reported. However, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Our results indicate that the three-dimensional time-resolved brain modeling method approaches the realistic human brain providing helpful information for NIRS systematic design and calibration for an individualized case with prior MRI data. To investigate individual differences in brain structure with spatial sensitivity profiles by NIRS measurement, two brain models from an adult and an aged volunteer were modeled to implement Monte Carlo simulation with various source-detector separations. The results indicate that NIRS measurement is a feasible and sensitive approach to structural signals generated in “brain atrophy.” Further, we propose a novel approach that uses near-infrared brain volumetric imaging to detect volumetric brain changes. The healthy, aged, and typical Alzheimer’s disease brains were modeled using the different characterization of volumetric brain changes to investigate the related features among these three brains with Monte Carlo simulation. Our study shows that near-infrared brain volumetric imaging can indicate brain atrophy for the clinical application of neurodegenerative diseases with patient-oriented measurement. We also demonstrate the clinical application of functional NIRS for discriminant analysis of schizophrenia. We performed statistical analyses of the prefrontal cortex (PFC) functional optical topography (fOT) image with a verbal fluency test. The imaging results between schizophrenia and healthy controls were significantly different, especially in the left PFC. According to our results, near-infrared diffuse optical imaging, with its advantages, could be a helpful clinical and research tool for patient-oriented diagnosis. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T23:14:44Z (GMT). No. of bitstreams: 1 ntu-101-D95543004-1.pdf: 4869413 bytes, checksum: 4d96e944af48e3f1d98eea2763e49580 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………………………………….I
Abstract……………………………………………………………………II 中文摘要………………………………………………………………….V Chapter 1 Introduction 1.1 Fundamentals of near-infrared spectroscopy and diffuse optical method………………………...............................................……...1 1.1.1 Optical properties of biological tissues…………………………2 1.1.2 Light-tissue interaction………………………………………….5 1.1.2.1 Light absorption……………………………………………5 1.1.2.2 Light scattering…………………………………………...8 1.1.3 Near-infrared spectroscopy……………………………………12 1.1.4 Diffuse optical tomography……………………………………15 1.2 Monte Carlo modeling……………………………………………...18 1.3 Modified Beer-Lambert’s law…………………………………...…21 Chapter 2 Photon migration of the human brain and optical brain imaging 2.1 Patient-oriented simulation based on Monte Carlo algorithm by using MRI data……………………………………………………………24 2.1.1 Brain modeling and results……………………………………26 2.1.2 Discussion……………………………………………………..41 2.2 Brain structure and spatial sensitivity profile assessing…………....45 2.2.1 Results of spatial sensitivity profile of MRI data set………….48 2.2.2 Results of source-detector optimization………………………53 2.2.3 Results of brain structural information assessing……………..56 2.2.4 Discussion……………………………………………………..59 2.3 Near-infrared brain volumetric imaging…………………………..61 2.3.1 Diffuse optical imaging for structural characterization of brain and results……………………………………………………...63 2.3.2 Discussion……………………………………………………..73 Chapter 3 Near-infrared functional brain imaging for schizophrenia diagnosis 3.1 Neuroimaging techniques…………………………………………..78 3.2 Discriminant analysis of functional optical topography for schizophrenia diagnosis…………..………………………….........82 3.2.1 fOT measurement with VFT…………………………………..83 3.2.2 Experimental results………………………..…………………90 3.2.3 Discussions……………………………………..……………...93 Chapter 4 Conclusions……………………….……………..…………...96 Chapter 5 Future works………………………………………………..100 References…………………………………………………………….101 Publication List……………………………………………………………a List of Figures Fig. 1.1 Absorption spectra of the main tissue constituents………………..3 Fig. 1.2 Illustration of photon migration in a turbid medium………………4 Fig. 1.3 Modes of light-tissue interaction…………………………………..5 Fig. 1.4 Specific absorption spectra………………………..………………7 Fig. 1.5 Diagram of Beer-Lambert’s absorption………………………….13 Fig. 1.6 The principle of three types of diffuse optical systems…………17 Fig. 2.1 Three dimensional in vivo MRI T1 brain image…………………27 Fig. 2.2 Segmentation of scalp and skull layer……………………………28 Fig. 2.3 Segmentation of CSF, gray matter and white matter…………….33 Fig. 2.4 The 3D brain model with five layers…………………….……….34 Fig. 2.5 The geometric configuration of source-detector…………………35 Fig. 2.6 The tomograms with different depths of the brain……….…….36 Fig. 2.7 The photon migration in the horizontal cross-section…..…...…37 Fig. 2.8 The spatial sensitivity profile…………………………………….39 Fig. 2.9 The curve of intensity distribution with multi-wavelength………40 Fig. 2.10 The ratios of the intensities of backscattered light……………...41 Fig. 2.11 The brain model from MRI data of adult and aged brai………...50 Fig. 2.12 Spatial sensitivity profiles in the human brain……………...…..52 Fig. 2.13 Spatial sensitivity profiles varied with received intensity……...53 Fig. 2.14 The ratio of the received intensity from different brain layer…..55 Fig. 2.15 The tissue volume ratio in adult and aged brains…….…………57 Fig. 2.16 Brain structure detected in sagittal view………………………..58 Fig. 2.17 The flowchart of brain modeling process…………………….65 Fig. 2.18 The source-detector separations on the human head…..………..66 Fig. 2.19 Optical brain models of healthy, aged and AD…………………67 Fig. 2.20 The ratios of tissue volume in healthy, aged and AD brains……68 Fig. 2.21 The dynamics of photon migration……………………………..69 Fig. 2.22 The detected intensities via various source-detector separation..70 Fig. 2.23 Normalized intensity curves via source-detector separations...72 Fig. 2.24 The attenuation index in healthy, aged, and AD brains…….…...72 Fig. 3.1 One-compartment model of neurovascular coupling………...…..80 Fig. 3.2 Comparing spatial and temporal sensitivities……………………81 Fig. 3.3 fOT channels localization………...………….……………..……86 Fig. 3.4 The sequence of the VFT…………………………………..…….87 Fig. 3.5 Flowchart of fOT data analyses…………..……………..……….88 Fig. 3.6 Group-level [oxy-Hb] mapping…………………………..……...91 Fig. 3.7 The results of group-level statistics on averaging………....……91 Fig. 3.8 The result of two sample Kolmogorov-Smirnov test……..……...92 List of Tables Table 2.1 Optical properties of brain tissues……………………………...36 Table 3.1 Clinical characteristics of the participant…..……………...…...85 Table 3.2 The four possible outcomes……………………………..……...93 | - |
| dc.language.iso | en | - |
| dc.subject | 蒙地卡羅模擬 | zh_TW |
| dc.subject | 精神分裂症鑑別分析 | zh_TW |
| dc.subject | 大腦體積造影 | zh_TW |
| dc.subject | 擴散光學造影 | zh_TW |
| dc.subject | schizophrenic discriminant analysis | en |
| dc.subject | Near-infrared diffuse optical imaging | en |
| dc.subject | volumetric brain imaging | en |
| dc.subject | Monte Carlo simulation | en |
| dc.title | 近紅外光擴散光學腦造影技術研發 | zh_TW |
| dc.title | Development of Near-infrared Diffuse Optical Brain Imaging Technique | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 100-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 孫家偉 | zh_TW |
| dc.contributor.coadvisor | ; | en |
| dc.contributor.oralexamcommittee | 江惠華;陳志成;林啟萬;林慶波;藍祚鴻;蔡睿哲;李柏磊 | zh_TW |
| dc.contributor.oralexamcommittee | ;;;;;; | en |
| dc.subject.keyword | 擴散光學造影,大腦體積造影,蒙地卡羅模擬,精神分裂症鑑別分析, | zh_TW |
| dc.subject.keyword | Near-infrared diffuse optical imaging,volumetric brain imaging,Monte Carlo simulation,schizophrenic discriminant analysis, | en |
| dc.relation.page | 130 | - |
| dc.identifier.doi | 10.6342/NTU.2012.00037 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2012-08-03 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 醫學工程學系 | - |
| 顯示於系所單位： | 醫學工程學研究所 | |
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