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系統識別號 U0026-2308201616392000
論文名稱(中文) 應用經驗模態分解對思覺失調症腦部非等向擴散影像之研究
論文名稱(英文) The Study of Application of Empirical Mode Decomposition to Fractional Anisotropic Diffusion Image for Brain with Schizophrenia
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 104
學期 2
出版年 105
研究生(中文) 王儷臻
研究生(英文) Li-Chen Wang
學號 P86034098
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 口試委員-林灶生
口試委員-黃敏偉
口試委員-孫永年
口試委員-陳彥廷
指導教授-鄭國順
中文關鍵字 擴散張量造影  擴散非等向性  思覺失調症  經驗模態分解 
英文關鍵字 Diffusion tensor image  fractional anisotropy  schizophrenia  empirical mode decomposition 
學科別分類
中文摘要 思覺失調症(schizophrenia)是嚴重的神經精神失調,其特點為認知控制能力貧乏。早期思覺失調症僅被認為是心理疾病,但近年許多研究指出思覺失調症與大腦白質的完整性密切相關。擴散張量造影(diffusion tensor image)是以非侵入式的方式有效觀察大腦水分擴散情況,其擴散非等向性指標(fractional anisotropy)常被用來判斷白質的完整性。經驗模態分解(empirical mode decomposition)主要應用於非線性及非穩態的訊號處理。理論上本質模態函數(intrinsic mode function)的平均包絡線每一點皆須為零,但在實際執行時此條件不易達成,因此本研究藉由調整平均包絡線的門檻值取得較佳的本質模態函數。於本研究中使用經驗模態分解腦部擴散非等向性圖,並分析強化本質模態函數的擴散非等向性圖,探討思覺失調症患者與對照組白質間的差異,以及病人幻覺嚴重程度與神經的擴散非等向性之關係。由結果可以說明,以神經束為基礎的空間統計(tract-based spatial statistics)發現思覺失調症病患與控制組有顯著差異,且在前額皮層區域的差異性尤為明顯,並發現病人在上縱束與下縱束神經的擴散非等向性顯著地低於對照組,但病人的幻覺分數與上縱束和下縱束神經的擴散非等向相關性極低。本研究利用改良的經驗模態分解觀察思覺失調症,發現病患的神經連結與控制組相比明顯下降,尤其病患在前額皮層區的神經連結越顯貧乏。
英文摘要 Schizophrenia is a severe neuropsychiatric disorder and its hallmark is poor cognitive control. In the past, schizophrenia was just thought as mental illness, but many studies suggested that schizophrenia is intimately related to integrity of white matter in the recently. Diffusion tensor image is a non-invasive MR technique which measuring diffusion circumstances of water. Fractional anisotropy of DTI represents integrity of white matter. The empirical mode decomposition is adaptive and it works well for non-linear and non-stationary signal. The mean envelope of intrinsic mode function must be zero at any points in theory, but it is difficult to reach in program of EMD. Therefore, the study adjusts threshold of mean envelope to get better IMFs. FA image is decomposed by EMD and statistical analysis of FA image with enhanced IMF is applied to explore that the significant difference of white matter between schizophrenia patients and healthy controls and the relation between hallucination score and FA of specific tracts. The prefrontal cortex is related to cognitive control, psychosis occurs if their connections go awry. The study found that there are significant difference in FA between schizophrenia patients and healthy controls by tract-based spatial and the areas where FA reduce extremely significant are in prefrontal cortex. The significantly reduced FA in the superior longitudinal fasciculus and inferior longitudinal fasciculus of patients compared with healthy controls but hallucination score is not related to mean FA of SLF and ILF. To sum up, the study applied improved EMD on observation of schizophrenia patients’ white matter. It found that schizophrenia patients’ connection are significantly poor compared with healthy controls, and extremely significantly reduced connection in prefrontal cortex of schizophrenia patients. The method is expected to analyze more neuropathy illness.
論文目次 摘要 I
ABSTRACT II
LIST OF TABLES VI
LIST OF FIGURES VII
Chapter1 INTRODUCTION - 1 -
1.1 Introduction - 1 -
1.1.1 Schizophrenia - 1 -
1.1.2 Gray matter and white matter - 4 -
1.1.3 Diffusion tensor imaging - 5 -
1.1.4 Empirical mode decomposition - 9 -
1.2 Motivation and Purpose - 10 -
1.2.1 Motivation - 10 -
1.2.2 Purpose - 11 -
Chapter2 MATERIALS AND METHODS - 12 -
2.1 Participants - 12 -
2.2 Image parameters - 12 -
2.3 Image processing - 13 -
2.3.1 Image preprocessing - 14 -
2.3.2 Radon transform - 15 -
2.3.3 Hilbert-Huang transform (HHT) - 16 -
2.3.3.1 Empirical mode decomposition - 16 -
2.3.3.2 Intrinsic mode function - 18 -
2.3.4 Combination of filtered back projection and improved empirical mode decomposition - 19 -
2.4 Statistical analysis - 25 -
2.4.1 Tract-based spatial statistics - 25 -
2.4.2 Fractional anisotropy of tracts - 27 -
Chapter3 RESULTS - 29 -
3.1 The manipulation of program interface - 29 -
3.2 The stoppage criteria of sifting process - 30 -
3.3 Tract-Based Spatial Statistics - 35 -
3.4 Fractional anisotropy of tracts - 37 -
Chapter4 DISCUSSION - 43 -
Chapter5 CONCLUSION AND PROSPECTS - 46 -
REFERENCES - 47 -
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