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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鍾孝文 | |
dc.contributor.author | Duen-Pang Kuo | en |
dc.contributor.author | 郭敦邦 | zh_TW |
dc.date.accessioned | 2021-05-19T17:54:40Z | - |
dc.date.available | 2022-02-21 | |
dc.date.available | 2021-05-19T17:54:40Z | - |
dc.date.copyright | 2017-02-21 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-02-05 | |
dc.identifier.citation | Chapter 1 1. Hacke W, Schwab S, De Georgia M: Intensive care of acute ischemic stroke. Cerebrovascular Diseases 1994, 4(6):385-392. 2. Saver JL: Time is brain—quantified. Stroke 2006, 37(1):263-266. 3. ATLANTIS T: Association of outcome with early stroke treatment: pooled analysis of ATLANTIS, ECASS, and NINDS rt-PA stroke trials. The Lancet 2004, 363(9411):768-774. 4. Marler JR, Tilley B, Lu M, Brott TG, Lyden P, Grotta J, Broderick J, Levine S, Frankel M, Horowitz S: Early stroke treatment associated with better outcome The NINDS rt-PA Stroke Study. Neurology 2000, 55(11):1649-1655. 5. Chalela JA, Kidwell CS, Nentwich LM, Luby M, Butman JA, Demchuk AM, Hill MD, Patronas N, Latour L, Warach S: Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. The Lancet 2007, 369(9558):293-298. 6. Fiebach J, Schellinger P, Jansen O, Meyer M, Wilde P, Bender J, Schramm P, J uuml;ttler E, Oehler J, Hartmann M: CT and diffusion-weighted MR imaging in randomized order Diffusion-weighted imaging results in higher accuracy and lower interrater variability in the diagnosis of hyperacute ischemic stroke. Stroke 2002, 33(9):2206-2210. 7. Fink JN, Kumar S, Horkan C, Linfante I, Selim MH, Caplan LR, Schlaug G: The stroke patient who woke up clinical and radiological features, including diffusion and perfusion MRI. Stroke 2002, 33(4):988-993. 8. Olivot J-M, Mlynash M, Thijs VN, Purushotham A, Kemp S, Lansberg MG, Wechsler L, Gold GE, Bammer R, Marks MP: Geography, structure, and evolution of diffusion and perfusion lesions in Diffusion and perfusion imaging Evaluation For Understanding Stroke Evolution (DEFUSE). Stroke 2009, 40(10):3245-3251. 9. Marks MP, Olivot J-M, Kemp S, Lansberg MG, Bammer R, Wechsler LR, Albers GW, Thijs V: Patients with acute stroke treated with intravenous tPA 3–6 hours after stroke onset: correlations between MR Angiography findings and perfusion-and diffusion-weighted imaging in the defuse study 1. Radiology 2008, 249(2):614-623. 10. Schellinger PD, Thomalla G, Fiehler J, K ouml;hrmann M, Molina CA, Neumann-Haefelin T, Ribo M, Singer OC, Zaro-Weber O, Sobesky J: MRI-based and CT-based thrombolytic therapy in acute stroke within and beyond established time windows an analysis of 1210 patients. Stroke 2007, 38(10):2640-2645. 11. Schellinger PD, Fiebach JB, Hacke W: Imaging-based decision making in thrombolytic therapy for ischemic stroke present status. Stroke 2003, 34(2):575-583. 12. Iosif C, Oppenheim C, Trystram D, Domigo V, M eacute;der J-F: MR imaging–based decision in thrombolytic therapy for stroke on awakening: Report of 2 cases. American Journal of Neuroradiology 2008, 29(7):1314-1316. 13. Cho A-H, Sohn S-I, Han M-K, Lee DH, Kim JS, Choi CG, Sohn C-H, Kwon SU, Suh DC, Kim SJ: Safety and efficacy of MRI-based thrombolysis in unclear-onset stroke. Cerebrovascular Diseases 2008, 25(6):572-579. 14. Straka M, Albers GW, Bammer R: Real‐time diffusion‐perfusion mismatch analysis in acute stroke. Journal of Magnetic Resonance Imaging 2010, 32(5):1024-1037. 15. Bhagat YA, Hussain MS, Stobbe RW, Butcher KS, Emery DJ, Shuaib A, Siddiqui MM, Maheshwari P, Al-Hussain F, Beaulieu C: Elevations of diffusion anisotropy are associated with hyper-acute stroke: a serial imaging study. Magnetic resonance imaging 2008, 26(5):683-693. 16. Bhagat YA, Emery DJ, Shuaib A, Sher F, Rizvi NH, Akhtar N, Clare TL, Leatherdale T, Beaulieu C: The relationship between diffusion anisotropy and time of onset after stroke. Journal of Cerebral Blood Flow Metabolism 2006, 26(11):1442-1450. 17. Liu Y, D’Arceuil HE, Westmoreland S, He J, Duggan M, Gonzalez RG, Pryor J, De Crespigny AJ: Serial diffusion tensor MRI after transient and permanent cerebral ischemia in nonhuman primates. Stroke 2007, 38(1):138-145. 18. Sakai K, Yamada K, Nagakane Y, Mori S, Nakagawa M, Nishimura T: Diffusion tensor imaging may help the determination of time at onset in cerebral ischaemia. Journal of Neurology, Neurosurgery Psychiatry 2009, 80(9):986-990. 19. Beuf O, Jaillon F, Saint-Jalmes H: Small-animal MRI: signal-to-noise ratio comparison at 7 and 1.5 T with multiple-animal acquisition strategies. Magnetic Resonance Materials in Physics, Biology and Medicine 2006, 19(4):202-208. 20. Kang CK, Park CA, Kim KN, Hong SM, Park CW, Kim YB, Cho ZH: Non‐invasive visualization of basilar artery perforators with 7T MR angiography. Journal of Magnetic Resonance Imaging 2010, 32(3):544-550. 21. Gonen O, Liu S, Goelman G, Ratai EM, Pilkenton S, Lentz MR, Gonz aacute;lez RG: Proton MR spectroscopic imaging of rhesus macaque brain in vivo at 7T. Magnetic resonance in medicine 2008, 59(4):692-699. Chapter 2 1. Le Bihan D, Breton E: Imagerie de diffusion in-vivo par r eacute;sonance magn eacute;tique nucl eacute;aire. Comptes-Rendus de l'Acad eacute;mie des Sciences 1985, 93(5):27-34. 2. Merboldt K-D, Hanicke W, Frahm J: Self-diffusion NMR imaging using stimulated echoes. Journal of Magnetic Resonance (1969) 1985, 64(3):479-486. 3. Taylor D, Bushell M: The spatial mapping of translational diffusion coefficients by the NMR imaging technique. Physics in medicine and biology 1985, 30(4):345. 4. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M: MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986, 161(2):401-407. 5. Assaf Y, Ben‐Bashat D, Chapman J, Peled S, Biton I, Kafri M, Segev Y, Hendler T, Korczyn A, Graif M: High b‐value q‐space analyzed diffusion‐weighted MRI: Application to multiple sclerosis. Magnetic Resonance in Medicine 2002, 47(1):115-126. 6. Hoehn‐Berlage M: Diffusion‐weighted NMR imaging: application to experimental focal cerebral ischemia. NMR in Biomedicine 1995, 8(7):345-358. 7. Hatakenaka M, Soeda H, Yabuuchi H, Matsuo Y, Kamitani T, Oda Y, Tsuneyoshi M, Honda H: Apparent diffusion coefficients of breast tumors: clinical application. Magnetic Resonance in Medical Sciences 2008, 7(1):23-29. 8. Moseley M, Cohen Y, Mintorovitch J, Chileuitt L, Shimizu H, Kucharczyk J, Wendland M, Weinstein P: Early detection of regional cerebral ischemia in cats: comparison of diffusion‐and T2‐weighted MRI and spectroscopy. Magnetic resonance in medicine 1990, 14(2):330-346. 9. Reith W, Hasegawa Y, Latour LL, Dardzinski BJ, Sotak CH, Fisher M: Multislice diffusion mapping for 3-D evolution of cerebral ischemia in a rat stroke model. Neurology 1995, 45(1):172-177. 10. Moseley M, Kucharczyk J, Mintorovitch J, Cohen Y, Kurhanewicz J, Derugin N, Asgari H, Norman D: Diffusion-weighted MR imaging of acute stroke: correlation with T2-weighted and magnetic susceptibility-enhanced MR imaging in cats. American Journal of Neuroradiology 1990, 11(3):423-429. 11. Merino JG, Warach S: Imaging of acute stroke. Nature Reviews Neurology 2010, 6(10):560-571. 12. Bammer R: Basic principles of diffusion-weighted imaging. European journal of radiology 2003, 45(3):169-184. 13. Torrey HC: Bloch equations with diffusion terms. Physical Review 1956, 104(3):563. 14. Kim HJ, Choi CG, Lee DH, Lee JH, Kim SJ, Suh DC: High-b-value diffusion-weighted MR imaging of hyperacute ischemic stroke at 1.5 T. American journal of neuroradiology 2005, 26(2):208-215. 15. Le Bihan D, Basser PJ: Molecular diffusion and nuclear magnetic resonance. Diffusion and perfusion magnetic resonance imaging 1995:5-17. 16. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H: Diffusion tensor imaging: concepts and applications. Journal of magnetic resonance imaging 2001, 13(4):534-546. 17. Basser PJ, Mattiello J, LeBihan D: Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance, Series B 1994, 103(3):247-254. 18. Zhang J, van Zijl P, Mori S: Image contrast using the secondary and tertiary eigenvectors in diffusion tensor imaging. Magnetic resonance in medicine 2006, 55(2):439-449. 19. Song S-K, Sun S-W, Ramsbottom MJ, Chang C, Russell J, Cross AH: Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 2002, 17(3):1429-1436. 20. Song S-K, Sun S-W, Ju W-K, Lin S-J, Cross AH, Neufeld AH: Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 2003, 20(3):1714-1722. 21. Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK: Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magnetic Resonance in Medicine 2006, 55(2):302-308. 22. Basser P, Pierpaoli C: Microstructural features measured using diffusion tensor imaging. J Magn Reson B 1996, 111(3):209-219. 23. O’Sullivan M, Jones DK, Summers P, Morris R, Williams S, Markus H: Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology 2001, 57(4):632-638. 24. Bammer R, Augustin M, Strasser‐Fuchs S, Seifert T, Kapeller P, Stollberger R, Ebner F, Hartung HP, Fazekas F: Magnetic resonance diffusion tensor imaging for characterizing diffuse and focal white matter abnormalities in multiple sclerosis. Magnetic Resonance in Medicine 2000, 44(4):583-591. 25. Sakai K, Yamada K, Nagakane Y, Mori S, Nakagawa M, Nishimura T: Diffusion tensor imaging may help the determination of time at onset in cerebral ischaemia. Journal of Neurology, Neurosurgery Psychiatry 2009, 80(9):986-990. 26. McGehee BE, Pollock JM, Maldjian JA: Brain perfusion imaging: how does it work and what should I use? Journal of Magnetic Resonance Imaging 2012, 36(6):1257-1272. 27. Meier P, Zierler KL: On the theory of the indicator-dilution method for measurement of blood flow and volume. Journal of applied physiology 1954, 6(12):731-744. 28. Zierler KL: Theoretical basis of indicator-dilution methods for measuring flow and volume. Circulation Research 1962, 10(3):393-407. 29. Rempp KA, Brix G, Wenz F, Becker CR, G uuml;ckel F, Lorenz WJ: Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 1994, 193(3):637-641. 30. Hofmeijer J, Schepers J, Van der Worp H, Kappelle L, Nicolay K: Comparison of perfusion MRI by flow‐sensitive alternating inversion recovery and dynamic susceptibility contrast in rats with permanent middle cerebral artery occlusion. NMR in Biomedicine 2005, 18(6):390-394. 31. Wu O, Oslash;stergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG: Tracer arrival timing‐insensitive technique for estimating flow in MR perfusion‐weighted imaging using singular value decomposition with a block‐circulant deconvolution matrix. Magnetic resonance in medicine 2003, 50(1):164-174. 32. Kluytmans M, Van der Grond J, Viergever M: Gray matter and white matter perfusion imaging in patients with severe carotid artery lesions. Radiology 1998, 209(3):675-682. 33. Belliveau JW, Rosen BR, Kantor HL, Rzedzian RR, Kennedy DN, McKinstry RC, Vevea JM, Cohen MS, Pykett IL, Brady TJ: Functional cerebral imaging by susceptibility‐contrast NMR. Magnetic Resonance in Medicine 1990, 14(3):538-546. 34. Nighoghossian N, Berthezene Y, Meyer R, Cinotti L, Adeleine P, Philippon B, Froment J, Trouillas P: Assessment of cerebrovascular reactivity by dynamic susceptibility contrast-enhanced MR imaging. Journal of the neurological sciences 1997, 149(2):171-176. 35. Nighoghossian N, Berthezene Y, Philippon B, Adeleine P, Froment J, Trouillas P: Hemodynamic parameter assessment with dynamic susceptibility contrast magnetic resonance imaging in unilateral symptomatic internal carotid artery occlusion. Stroke 1996, 27(3):474-479. 36. Bhagat YA, Emery DJ, Shuaib A, Sher F, Rizvi NH, Akhtar N, Clare TL, Leatherdale T, Beaulieu C: The relationship between diffusion anisotropy and time of onset after stroke. Journal of Cerebral Blood Flow Metabolism 2006, 26(11):1442-1450. 37. Carano RA, Li F, Irie K, Helmer KG, Silva MD, Fisher M, Sotak CH: Multispectral analysis of the temporal evolution of cerebral ischemia in the rat brain. J Magn Reson Imaging 2000, 12(6):842-858. 38. Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG: Waxholm Space atlas of the Sprague Dawley rat brain. Neuroimage 2014, 97:374-386. 39. Jenkinson M, Bannister P, Brady M, Smith S: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002, 17(2):825-841. 40. Jenkinson M, Smith S: A global optimisation method for robust affine registration of brain images. Medical image analysis 2001, 5(2):143-156. Chapter 3 1. Hacke W, Kaste M, Bluhmki E, Brozman M, D aacute;valos A, Guidetti D, Larrue V, Lees KR, Medeghri Z, Machnig T: Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. New England Journal of Medicine 2008, 359(13):1317-1329. 2. Kang DW, Kwon JY, Kwon SU, Kim JS: Wake‐up or unclear‐onset strokes: are they waking up to the world of thrombolysis therapy? Int J Stroke 2012, 7(4):311-320. 3. Bhagat YA, Hussain MS, Stobbe RW, Butcher KS, Emery DJ, Shuaib A, Siddiqui MM, Maheshwari P, Al-Hussain F, Beaulieu C: Elevations of diffusion anisotropy are associated with hyper-acute stroke: a serial imaging study. Magn Reson Imaging 2008, 26(5):683-693. 4. Sakai K, Yamada K, Nagakane Y, Mori S, Nakagawa M, Nishimura T: Diffusion tensor imaging may help the determination of time at onset in cerebral ischaemia. J Neurol Neurosurg Psychiatry 2009, 80(9):986-990. 5. Puig J, Blasco G, Daunis-I-Estadella J, Thomalla G, Castellanos M, Soria G, Prats-Galino A, S aacute;nchez-Gonz aacute;lez J, Boada I, Serena J: Increased Corticospinal Tract Fractional Anisotropy Can Discriminate Stroke Onset Within the First 4.5 Hours. Stroke 2013, 44(4):1162-1165. 6. Shereen A, Nemkul N, Yang D, Adhami F, Dunn RS, Hazen ML, Nakafuku M, Ning G, Lindquist DM, Kuan C-Y: Ex vivo diffusion tensor imaging and neuropathological correlation in a murine model of hypoxia–ischemia-induced thrombotic stroke. Journal of Cerebral Blood Flow Metabolism 2011, 31(4):1155-1169. 7. Chiang T, Messing RO, Chou W-H: Mouse model of middle cerebral artery occlusion. Journal of visualized experiments: JoVE 2011(48). 8. Br aring;tane BT, Walvick RP, Corot C, Lancelot E, Fisher M: Characterization of gadolinium-based dynamic susceptibility contrast perfusion measurements in permanent and transient MCAO models with volumetric based validation by CASL. Journal of Cerebral Blood Flow Metabolism 2010, 30(2):336-342. 9. Henninger N, Bouley J, Nelligan JM, Sicard KM, Fisher M: Normobaric hyperoxia delays perfusion/diffusion mismatch evolution, reduces infarct volume, and differentially affects neuronal cell death pathways after suture middle cerebral artery occlusion in rats. Journal of Cerebral Blood Flow Metabolism 2007, 27(9):1632-1642. 10. Yao X, Yu T, Liang B, Xia T, Huang Q, Zhuang S: Effect of Increasing Diffusion Gradient Direction Number on Diffusion Tensor Imaging Fiber Tracking in the Human Brain. Korean J Radiol 2015, 16(2):410-418. 11. Oslash;stergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR: High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magnetic resonance in medicine 1996, 36(5):715-725. 12. Lee EK, Choi SH, Yun TJ, Kang KM, Kim TM, Lee S-H, Park C-K, Park S-H, Kim IH: Prediction of response to concurrent chemoradiotherapy with temozolomide in glioblastoma: application of immediate post-operative dynamic susceptibility contrast and diffusion-weighted MR imaging. Korean J Radiol 2015, 16(6):1341-1348. 13. Wang W, Steward C, Desmond P: Diffusion tensor imaging in glioblastoma multiforme and brain metastases: the role of p, q, L, and fractional anisotropy. Am J Neuroradiol 2009, 30(1):203-208. 14. Meng X, Fisher M, Shen Q, Sotak CH, Duong TQ: Characterizing the diffusion/perfusion mismatch in experimental focal cerebral ischemia. Annals of neurology 2004, 55(2):207-212. 15. Shen Q, Meng X, Fisher M, Sotak CH, Duong TQ: Pixel-by-pixel spatiotemporal progression of focal ischemia derived using quantitative perfusion and diffusion imaging. Journal of Cerebral Blood Flow Metabolism 2003, 23(12):1479-1488. 16. Bhagat YA, Emery DJ, Shuaib A, Sher F, Rizvi NH, Akhtar N, Clare TL, Leatherdale T, Beaulieu C: The relationship between diffusion anisotropy and time of onset after stroke. Journal of Cerebral Blood Flow Metabolism 2006, 26(11):1442-1450. 17. Carano RA, Li F, Irie K, Helmer KG, Silva MD, Fisher M, Sotak CH: Multispectral analysis of the temporal evolution of cerebral ischemia in the rat brain. J Magn Reson Imaging 2000, 12(6):842-858. 18. Papp EA, Leergaard TB, Calabrese E, Johnson GA, Bjaalie JG: Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage 2014, 97:374-386. 19. Jenkinson M, Bannister P, Brady M, Smith S: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002, 17(2):825-841. 20. Marquardt DW: An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics 1963, 11(2):431-441. 21. Henninger N, Bratane BT, Bastan B, Bouley J, Fisher M: Normobaric hyperoxia and delayed tPA treatment in a rat embolic stroke model. Journal of Cerebral Blood Flow Metabolism 2009, 29(1):119-129. 22. Singhal AB, Benner T, Roccatagliata L, Koroshetz WJ, Schaefer PW, Lo EH, Buonanno FS, Gonzalez RG, Sorensen AG: A pilot study of normobaric oxygen therapy in acute ischemic stroke. Stroke 2005, 36(4):797-802. 23. Kim HY, Singhal AB, Lo EH: Normobaric hyperoxia extends the reperfusion window in focal cerebral ischemia. Ann Neurol 2005, 57(4):571-575. 24. Flynn EP, Auer RN: Eubaric hyperoxemia and experimental cerebral infarction. Ann Neurol 2002, 52(5):566-572. 25. Yam P, Dewar D, McCulloch J: Axonal injury caused by focal cerebral ischemia in the rat. Journal of neurotrauma 1998, 15(6):441-450. 26. Dewar D, Dawson DA: Changes of cytoskeletal protein immunostaining in myelinated fibre tracts after focal cerebral ischaemia in the rat. Acta neuropathologica 1996, 93(1):71-77. 27. Kuroiwa T, Nagaoka T, Ueki M, Yamada I, Miyasaka N, Akimoto H: Different apparent diffusion coefficient water content correlations of gray and white matter during early ischemia. Stroke 1998, 29(4):859-865. 28. Pantoni L, Garcia JH, Gutierrez JA: Cerebral white matter is highly vulnerable to ischemia. Stroke 1996, 27(9):1641-1647. 29. Armitage GA, Todd KG, Shuaib A, Winship IR: Laser speckle contrast imaging of collateral blood flow during acute ischemic stroke. J Cereb Blood Flow Metab 2010, 30(8):1432-1436. 30. Zhang H, Prabhakar P, Sealock R, Faber JE: Wide genetic variation in the native pial collateral circulation is a major determinant of variation in severity of stroke. Journal of Cerebral Blood Flow Metabolism 2010, 30(5):923-934. 31. Cowper SE, Robin HS, Steinberg SM, Su LD, Gupta S, LeBoit PE: Scleromyxoedema-like cutaneous diseases in renal-dialysis patients. The Lancet 2000, 356(9234):1000-1001. 32. Takasawa M, Jones PS, Guadagno JV, Christensen S, Fryer TD, Harding S, Gillard JH, Williams GB, Aigbirhio FI, Warburton EA: How reliable is perfusion MR in acute stroke? Validation and determination of the penumbra threshold against quantitative PET. Stroke 2008, 39(3):870-877. 33. Calamante F, Gadian D, Connelly A: Quantification of perfusion using bolus tracking magnetic resonance imaging in stroke assumptions, limitations, and potential implications for clinical use. Stroke 2002, 33(4):1146-1151. 34. Wu O, Oslash;stergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG: Tracer arrival timing‐insensitive technique for estimating flow in MR perfusion‐weighted imaging using singular value decomposition with a block‐circulant deconvolution matrix. Magnetic resonance in medicine 2003, 50(1):164-174. 35. SAKAI K, YAMADA K, OOUCHI H, NISHIMURA T: Numerical simulation model of hyperacute/acute stage white matter infarction. Magn Reson Med Sci 2008, 7(4):187-194. 36. Pierpaoli C, Basser PJ: Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996, 36(6):893-906. 37. Kim SJ, Choi CG, Kim JK, Yun S-C, Jahng G-H, Jeong H-K, Kim EJ: Effects of MR Parameter Changes on the Quantification of Diffusion Anisotropy and Apparent Diffusion Coefficient in Diffusion Tensor Imaging: Evaluation Using a Diffusional Anisotropic Phantom. Korean J Radiol 2015, 16(2):297-303. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7817 | - |
dc.description.abstract | 急性缺血性中風佔所有中風型態約80%,主要是因為供應腦組織血流的血管阻塞而引起的,會造成病人死亡或癱瘓。若沒有及時將阻塞血管的血栓打通,缺血半影區(尚存活著的腦組織)將逐漸消失並轉變成不可逆的梗塞中心。 1995年,美國國家神經及中風疾病研究院的研究報告指出: 發生急性缺血性中風後3小時內,使用靜脈注射血栓溶解劑治療可以有效地改善病人的神經功能並得到有利的治療結果。近年來,更有臨床試驗(例如ECASS-III,SITS-ISTR)指出病人可以在發病後3至4.5小時內使用血栓溶解治療,亦可從中獲得治療效果。不幸的是,大多數中風病人,其確切的發病時間是不確定的,將會被排除在血栓溶解治療之外。 此外,在進行任何形式的血栓溶解治療之前,確定是否存在相當體積且能被救活的腦組織,也是一個重要的考量。對於不知道發病時間且將被排除在治療之外的病人,若能考量到其中風情況(例如:存在能救活組織的多寡),而不單以發病時間作為唯一考量,應可使血栓溶解治療嘉惠更多病人。而現今的造影科技例如電腦斷層攝影或磁振造影,都可幫助臨床醫生藉由影像選出適合血栓溶解的病人。在磁振造影的成像技術中,擴散加權成像和灌注加權成像是可用於評估是否存在能被救活的組織的技術。利用灌注與擴散影像不匹配的概念,臨床醫生可以找出尚存活著組織的多寡,參考並評估超過治療時限的病人是否也能接受溶栓治療。然而,在臨床實務上,病人能接受治療的時間非常急迫,目前的磁振造影後處理決定半影區的方法耗時耗力,對於病人時常緩不濟急,時間是關鍵,任何診斷方法必須快速地完成。 近年來,磁振造影的另一種成像技術,如擴散張量成像,已經成為研究缺血性中風的有利的工具。擴散張量成像能夠描繪出因缺血性中風所導致腦部微結構的細微變化,通常可用擴散張量的非等向性(fractional anisotropy, FA)來表示。在此研究中,我們認為缺血半影區和梗塞中心血流缺損不同,所引起的細胞損傷也應不同,所反映的FA是否也不同?是否可區分缺血半影區和梗塞中心?我們也進一步假設FA的變化可以用於推測中風的發病時間。為驗證假設,我們在7T磁振造影中建立大鼠永久中大腦動脈梗塞的模型,並重複擴散張量成像序列,分別觀察缺血半影區和梗塞中心FA的變化。另一方面,由於FA是由純擴散非等向性(pure anisotropic diffusion ,q) 和擴散強度(diffusion magnitude, L)的比例來定義,我們也分開研究q和L以更完整地描述缺血性中風環境的微結構變化。也測試了在純氧環境下q和L隨時間的變化。結果發現,可以利用L值的來區別缺血半影區和梗塞中心。此外,與正常半腦側相比,q值減少若小於44.6%可推測中風發生時間小於4.5小時。 我們的結論是,擴散張量成像可以快速且可靠的利用L值來區別缺血半影區和梗塞中心,並利用q值來估計中風時間,可提供臨床醫生在進行血栓溶解或要保守治療的參考資訊。 | zh_TW |
dc.description.abstract | Acute ischemic stroke (AIS), which constitutes approximately 80% of overall strokes, is a major cause of death and disability due to blockage of blood supply to the brain tissue. Without early recanalization, the ischemic penumbra (IP) (i.e., the area that is at risk for infarction) will gradually diminish and then turn into irreversible infarct core (IC) with time. In 1995, the National Institute of Neurological Disorders and Stroke (NINDS) study group reported that treatment with intravenous recombinant tissue plasminogen activator (rtPA) within 3 hours of the onset of AIS can effectively improve the patients’ neurological function and result in a favorable outcome at3 months. In recent years, there have been a number of clinical trials (e.g. ECASS-III, SITS-ISTR) investigating the therapeutic time window beyond 3 of the onset of AIS. For a subgroup of patients with AIS, for example, wake-up stroke, the exact time of onset is uncertain and then would be excluded from rtPA treatment. In addition, an important consideration is to determine whether the substantial salvageable brain tissue is present before any form of thrombolytic therapy. For patients excluded from rtPA treatment according to current guidelines, the selection of patients for thrombolysis may be made more efficacious by considering individual salvageable tissue rather than relying solely on the onset time as the determinant of selection. The current imaging modality such as computed tomography (CT) or magnetic resonance imaging (MRI) can comprehensively detect and characterize AIS to help physicians with the selection of appropriate candidates for thrombolysis. In MRI techniques, diffusion-weighted imaging (DWI) and perfusion-weighted imagings (PWI) are two powerful techniques used to evaluate whether the salvageable brain tissue is present. With the use of concept of PWI/DWI mismatch of greater than 20%, clinicians can define the salvageable tissue, which may be considered for selecting patients eligible for thrombolysis beyond time windows. However, the image data processing could be very time-consuming and might not be appropriate in the AIS setting. Because time is critical, an effective diagnostic imaging method is desirable for clinical decision-making. In recent years, another imaging technique of MRI such as diffusion tensor imaging (DTI) has emerged as a promising tool to study AIS. DTI has shown to be capable of delineating in the micro-structural changes of brain due to ischemic stroke-induced damage, commonly expressed as the fractional anisotropy (FA). In this study, we first hypothesize that the DTI metric changes may differ in IP and IC regions due to ischemic injury as a result of different extents of perfusion deficit. We further hypothesize that the evolution of DTI metric changes may be used to predict the onset time of AIS. To verify hypotheses, we established permanent middle cerebral artery occlusion (MCAo) model in rat at 7T MRI and then DTI sequences were performed repeatedly after MCAo for longitudinal observation of FA changes from IP and IC, respectively. Because the FA is a relative scalar value defined by the ratio of pure anisotropic diffusion (q) and diffusion magnitude (L), we measured the parameters q and L separately in affected ipsilateral and unaffected contralateral sides and calculated differences between the ipsi- and contralateral side (r) for more complete picture of diffusion changes in AIS. We also test the effect of oxygenation in the evolution of q and L. The study found that discrimination of IP from IC by rL values showed comparable results to the conventional PWI/DWI mismatch. Additionally, by regression analysis, the stroke age of 4.5 hours can be estimated by an rq value of -44.6% (a 44.6% reduction) in the cortical IC regions. In conclusion, our preliminary results suggest that a single DTI could provide a quick and reliable measure to distinguish IP from IC based on the L values, and estimate stroke age using the q values, thus potentially provide valuable information to the treating physicians considering whether to treat acute stroke by means of thrombolytic therapy or conservative management. | en |
dc.description.provenance | Made available in DSpace on 2021-05-19T17:54:40Z (GMT). No. of bitstreams: 1 ntu-106-D95921026-1.pdf: 2606215 bytes, checksum: 6bc778bbfd652c75963a7c4f19e8c687 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 中文摘要……………………………………………………………………….....…...... i 英文摘要……………………………………………………………………………..... iii List of figures……………………………………………………………………….......ix List of tables………………………………………………………………………….….x Chapter 1 Introduction References………………………………………………………………………....1-5 Chapter 2 Methods 2.1 Diffusion tensor imaging and the anisotropy index…….....…………………..2-1 2.2 Dynamic susceptibility contrast-enhanced technique.………….……..…......2-10 2.3 Topographic classification of brain tissue types………….………………….2-15 2.4 References……………….………………………………………………...…2-22 Chapter 3 Using DTI metrics in animal stroke model 3.1 stroke model in normobaric hyperoxia……………………………………..….3-1 3.1.1 Introduction………………………………………………………….…..3-1 3.1.2 Animal preparation and data analysis …………………………..………3-4 3.1.3 Results……………………………………………………………….…..3-9 3.1.4 Discussion and conclusion……………………………………...….…..3-12 3.2 stroke model in room air ……………………………………………...….….3-26 3.2.1 Introduction……………………………………………………...….….3-26 3.2.2 Animal preparation and data analysis……………………..…….……..3-27 3.2.3 Results…………………………………………………………...……..3-31 3.2.4 Discussion and conclusion…………………………………….………3-32 3.3 References……………………………………………………………….…..3-35 Chepter 4 Conclusion | |
dc.language.iso | en | |
dc.title | 利用擴散張量成像區分梗塞中心與缺血半影區: 動物模型 | zh_TW |
dc.title | Differentiation of the Infarct Core from Ischemic Penumbra using Diffusion Tensor Imaging: A Rat Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-1 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 陳震宇 | |
dc.contributor.oralexamcommittee | 彭家勛,盧家鋒,吳文超,郭萬祐,王福年 | |
dc.subject.keyword | 急性缺血性中風,缺血半影區,梗塞中心,擴散加權成像,灌注加權成像,擴散張量成像, | zh_TW |
dc.subject.keyword | acute ischemic stroke,ischemic penumbra,infarct core,diffusion-weighted imaging,perfusion-weighted imaging,diffusion tensor imaging, | en |
dc.relation.page | 93 | |
dc.identifier.doi | 10.6342/NTU201700312 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2017-02-05 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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