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論文名稱 Title |
表面式與體素式大腦皮質厚度量測方法之比較:應用於極低體重早產之青少年 Comparison of surface-based and voxel-based cerebral cortical thickness measurements: application on very-low-birth-weight teenagers born preterm |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
76 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2016-01-22 |
繳交日期 Date of Submission |
2016-01-29 |
關鍵字 Keywords |
早產兒、大腦皮質厚度、極低出生體重、再現性 Reproducibility, very low birth weight, preterm born children, Cerebral cortical thickness |
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統計 Statistics |
本論文已被瀏覽 5692 次,被下載 463 次 The thesis/dissertation has been browsed 5692 times, has been downloaded 463 times. |
中文摘要 |
大腦皮質厚度可以提供臨床上重要的資訊,並且診斷特定神經退化疾病嚴重程度,現今自動化量測方法可分為Surface-based或Voxel-based方式,本研究比較三種量測方法的再現性程度,分別為Surface-based方式中的FreeSurfer和Voxel-based方式中的DiReCT、Laplace-VBCT兩種。再現性分析於單一受試者重複進行MR掃瞄時,比較全域及區域皮質的厚度變異係數大小,結果顯示三種方法於全域比較中再現性程度相差不大,在根據DKT atlas所進行的皮質分區比較中,FreeSurfer相較於兩種Voxel-based方法在絕大數的區域中具有最小的變異係數。 此外,本研究使用這三種量測方法,對於15位具有極低出生體重且排除腦部傷害的早產青少年(平均年齡12.8歲)與17位同年齡出生體重正常的對照組(平均年齡13.8歲)進行大腦皮質厚度分析。結果顯示不管使用何種量測方法,早產組相對於同齡對照組在部分區域發現較厚皮質,且只有在使用Laplace-VBCT方法時才發現有極小區域有較薄的皮質。若將受試者採用13歲分為高低次群組比較,早產兒皮質較厚的現象在低齡群組的比較中依舊存在,在高齡群組中反而出現較厚和較薄區域共陳的結果。發現支持人腦大腦皮質於青春期發展有皮質修剪現象,且極低出生體重早產兒有延遲的大腦成長,所以早產兒之皮質修剪現象相對同齡對照組較慢發生,但隨著歲數增長,兩群組之間差異縮小。 |
Abstract |
Cerebral cortical thickness can provide important clinical information, helping the diagnosis of specific neurodegenerative diseases. In general, the automatic cortical thickness measurement methods can be classified into two types, surface-based and voxel-based. In this study, three methods, including one surface-based FreeSurfer and two voxel-based methods, DiReCT and Laplace-VBCT, are compared. A single subject was scanned for 24 times in a period of three and half months. The global and regional mean of cortical thickness are obtained for each experiment to exam the reproducibility. Results show that the coefficients of variance (CV) of three methods are very close to each other while FreeSurfer is found to have the smallest CV in most of DKT cortical regions than two other voxel-based methods. In addition, magnetic resonance imaging experiments were performed on 15 young teenagers very-low-birth-weight born preterm (mean age 12.8 yr), all without brain injury, and age-matched 17 normal peers (mean age 13.8 yr) to assess the cerebral cortical thickness. The two-sample t-test with a p value of 0.05 is used to detect the statistical difference. Thicker cortical thickness was found in parietal, temporal, and occipital lobes of the preterm group by using all three measurement methods. In a sub-group comparison, thicker cortices are found in similar areas of young preterm compared with young control. However, when the old preterm is compared to the old control group, more cortical regions with thinner thickness are found than those with thicker cortex. In addition, the young preterm group has thinner cortex than old preterm, revealing the cortical thinning during childhood and early adolescence, which can be also observed but in much less area. All of our results suggest that the developmental cortical thinning of preterm born children is delayed to their term born peers, but the group deviation of cortical thickness is narrowed with increasing age. |
目次 Table of Contents |
論文審定書 i 致謝 ii 摘要 iii Abstract iv 目錄 vi 圖目錄 viii 表目錄 x 第一章 介紹 1 1.1 背景 1 1.2 文獻探討 4 1.2.1 皮質自動化量測方法 4 1.2.2 皮質厚度發展曲線 6 1.2.3 早產兒皮質厚度 9 1.3 研究動機 11 第二章 資料與方法 12 2.1 影像資料 12 2.2 皮質厚度量測方法 14 2.2.1 Surface-based : FreeSurfer 14 2.2.2 Voxel-based : Laplace-VBCT演算法 19 2.2.3 Voxel-based : DiReCT演算法 23 2.2.4 皮質厚度量測流程 25 2.3 大腦皮質分區 26 2.4 實驗設計 28 第三章 實驗結果 30 3.1 再現性分析結果 30 3.1.1 全域(Global)皮質厚度分析 30 3.1.2 區域(Region)皮質厚度分析 32 3.2 早產兒皮質厚度分析結果 37 3.2.1分析流程說明 37 3.2.2 Vertex-wise分析 38 3.2.3 Region-wise分析 44 第四章 討論與結論 57 4.1 再現性分析討論 57 4.2 早產兒皮質厚度分析討論 58 4.3 結論 61 參考文獻 62 |
參考文獻 References |
1. Jones, S.E., B.R. Buchbinder, and I. Aharon, Three-dimensional mapping of cortical thickness using Laplace's equation. Hum Brain Mapp, 2000. 11(1): p. 12-32. 2. The Cytoarchitectonics of the Human Cerebral Cortex. Journal of Anatomy, 1929. 63(Pt 3): p. 389-389. 3. Chung, M.K., et al., Cortical thickness analysis in autism with heat kernel smoothing. Neuroimage, 2005. 25(4): p. 1256-65. 4. Zielinski, B.A., et al., Longitudinal changes in cortical thickness in autism and typical development. Brain, 2014. 137(Pt 6): p. 1799-812. 5. Shaw, P., et al., Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch Gen Psychiatry, 2006. 63(5): p. 540-9. 6. Almeida Montes, L.G., et al., Brain cortical thickness in ADHD: age, sex, and clinical correlations. J Atten Disord, 2013. 17(8): p. 641-54. 7. Walhovd, K.B., et al., Blood markers of fatty acids and vitamin D, cardiovascular measures, body mass index, and physical activity relate to longitudinal cortical thinning in normal aging. Neurobiol Aging, 2014. 35(5): p. 1055-64. 8. Luders, E., et al., Gender effects on cortical thickness and the influence of scaling. Hum Brain Mapp, 2006. 27(4): p. 314-24. 9. MacDonald, D., et al., Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage, 2000. 12(3): p. 340-56. 10. Fischl, B. and A.M. Dale, Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A, 2000. 97(20): p. 11050-5. 11. Hutton, C., et al., Voxel-based cortical thickness measurements in MRI. Neuroimage, 2008. 40(4): p. 1701-10. 12. Das, S.R., et al., Registration based cortical thickness measurement. Neuroimage, 2009. 45(3): p. 867-79. 13. Winkler, A.M., et al., Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 2010. 53(3): p. 1135-46. 14. Clarkson, M.J., et al., A comparison of voxel and surface based cortical thickness estimation methods. Neuroimage, 2011. 57(3): p. 856-65. 15. Acosta, O., et al., Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian-Eulerian PDE approach using partial volume maps. Med Image Anal, 2009. 13(5): p. 730-43. 16. Li, Q., et al., Cortical thickness estimation in longitudinal stroke studies: A comparison of 3 measurement methods. Neuroimage Clin, 2015. 8: p. 526-35. 17. Tustison, N.J., et al., Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage, 2014. 99: p. 166-79. 18. Lerch, J.P. and A.C. Evans, Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage, 2005. 24(1): p. 163-73. 19. Shaw, P., et al., Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci, 2008. 28(14): p. 3586-94. 20. Sowell, E.R., et al., Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci, 2004. 24(38): p. 8223-31. 21. Volpe, J.J., Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol, 2009. 8(1): p. 110-24. 22. Martinussen, M., et al., Cerebral cortex thickness in 15-year-old adolescents with low birth weight measured by an automated MRI-based method. Brain, 2005. 128(Pt 11): p. 2588-96. 23. Bjuland, K.J., et al., Cortical thickness and cognition in very-low-birth-weight late teenagers. Early Hum Dev, 2013. 89(6): p. 371-80. 24. Nagy, Z., H. Lagercrantz, and C. Hutton, Effects of preterm birth on cortical thickness measured in adolescence. Cereb Cortex, 2011. 21(2): p. 300-6. 25. Zubiaurre-Elorza, L., et al., Cortical Thickness and Behavior Abnormalities in Children Born Preterm. PLOS ONE, 2012. 26. Solsnes, A.E., et al., Cortical morphometry and IQ in VLBW children without cerebral palsy born in 2003-2007. Neuroimage Clin, 2015. 8: p. 193-201. 27. Murner-Lavanchy, I., et al., Delay of cortical thinning in very preterm born children. Early Hum Dev, 2014. 90(9): p. 443-50. 28. Collins, D.L., et al., Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr, 1994. 18(2): p. 192-205. 29. Segonne, F., et al., A hybrid approach to the skull stripping problem in MRI. Neuroimage, 2004. 22(3): p. 1060-75. 30. 鍾潤宏, 體素式大腦皮質厚度量測之穩定性分析, in 電機工程學系研究所. 2012, 國立中山大學: 高雄市. p. 60. 31. Klein, A. and J. Tourville, 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci, 2012. 6: p. 171. 32. Grunewaldt, K.H., et al., Follow-up at age 10 years in ELBW children - functional outcome, brain morphology and results from motor assessments in infancy. Early Hum Dev, 2014. 90(10): p. 571-8. |
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