應用希爾伯特黃轉換於功能性磁振造影之非穩態信號分析

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姓名

林耕宏(Geng-Hong Lin) 查詢紙本館藏

畢業系所

生物醫學工程研究所

論文名稱

應用希爾伯特黃轉換於功能性磁振造影之非穩態信號分析
(Implementation of Hilbert-Huang Transform On Non-stationary Functional MRI Signal)

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摘要(中)

典型的功能性磁振造影(fMRI)分析一般皆奠基於線性假設的血液動力模型非時變性的特性並且專注於群體間差異的結果。近年來靜息態功能性磁振造影這一類新型的分析技術引起了原本較少探討的大腦的動態變化以及個體差異等新興議題。在目前的動態分析方法之中，希爾伯特黃轉換(HHT)建立在非線性以及非穩態架構之下，不僅提供了較佳的時頻分析解析度以及在生醫訊號中所可以代表著生理意義。然而傳統HHT分析方法原為針對一維的時間信號進行分析，鮮少用在四維的功能性磁振造影信號上。因此在論文初步應用HHT動態分析技術功能性磁振造影以探討大腦在運作或靜止時的動態變化。
具體來說，HHT是由經驗模態分解法(EMD)以及希爾伯特時頻分析(HSA)兩部分所構成。我們嘗試利用EMD於提升fMRI信號之敏感度，並以HSA觀察信號的時頻變化。在第一部分中，我們利用整體經驗模態分解法(EEMD)取代傳統的經驗模態分解法以解構原始fMRI信號，因為EEMD對於fMRI生理雜訊的干擾較不敏感。我們利用了四種在fMRI信號上可能發生的非穩態模擬訊號來評估EEMD是否能有效的應用於fMRI。在第二部分中，利用HSA觀察手指運動時以及靜息態磁振造影狀態時fMRI訊號的動態變化，並與wavelet時頻分析進行比較。另外，由於EEMD處理fMRI信號分析上的時間過於冗長，我們利用圖形運算單元(GPU)對於EEMD分析在MATLAB程式平台上進行加速。根據以上總述，在本論文中我們成功將HHT應用在fMRI資料處理，可確實提升fMRI訊號靈敏度，並可解析更細微的大腦動態資訊。

摘要(英)

Traditionally, functional Magnetic Resonance Image (fMRI) is based on the observation of hemodynamic response function (HRF), and the fMRI analysis follows the assumptions including the time invariance and group analysis. Recently, the resting-state fMRI technology attracts public attention and addressed new issues like brain dynamics and individual differences, which are rarely explored. Among all dynamic analyses methods, the Hilbert-Huang Transform (HHT), based on non-linearity and non-stationary, not only provides better resolutions in both time and frequency domains, but provides physiological meanings in biomedical signals. However, original HHT was used to decompose one dimensional signal and hardly applied to the 4D fMRI signal. Therefore, in this thesis, we preliminarily applied the HHT to both task-based and resting-state fMRI data to extract the dynamic information in brain circuits.
Specifically, because HHT is composed of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), we attempts to apply both EMD and HSA on fMRI dataset for two purposes: contrast enhancement and dynamic analysis, respectively. In the first part, we applied Ensemble Empirical Mode Decomposition (EEMD) to replace EMD because EEMD is relatively insensitive to the intrinsic noise of fMRI signal. We conducted four types of non-stationary simulations of fMRI signal to evaluate the effectiveness of EEMD. In the second part, we performed HSA analyses on both finger-tapping and resting-state fMRI datasets to observe the dynamic process within different brain regions, and compared the results with wavelet analysis. In addition, because of the long processing time of EEMD analysis, we applied the parallel computing of Graphic Processing Unit (GPU) for EEMD acceleration on the MATLAB platform. In summary, we successfully applied HHT on fMRI datasets for enhancing the sensitivity to task-induced activation and for exploring the detailed brain dynamics among brain areas.

關鍵字(中)

★ 功能性磁振造影(fMRI)
★ 希爾伯特-黃轉換(HHT)
★ 整體經驗模態分解法(EEMD)
★ 時頻分析
★ 希爾伯特時頻分析
★ 大腦動態變化
★ 非穩態分析

關鍵字(英)

★ functional MRI (fMRI)
★ Hilbert-Huang transform (HHT)
★ ensemble empirical mode decomposition (EEMD)
★ Hilbert-spectral analysis
★ time-frequency analysis
★ brain dynamics
★ nonstationarity

論文目次

中文摘要 i
英文摘要 iii
致謝 v
目錄 vi
圖目錄 viii
表目錄 ix
第一章緒論 1
1.1 研究動機 1
1.2 研究目的與方法 2
第二章研究理論與研究背景 4
2.1 功能性磁振造影(fMRI)原理 4
2.2 希爾伯特黃轉換(HHT)理論 6
2.3 經驗模態分解法(Empirical Mode Decomposition, EMD) 7
2.4 整體經驗模態分解法(Ensemble Empirical Mode Decomposition, EEMD) 10
2.5 希爾伯特頻譜(Hilbert Spectrum) 11
2.6 GPU運算與CUDA 簡介 13
第三章實驗材料與實驗方法 14
3.1 fMRI實驗程序 14
3.2 EEMD分析fMRI影像分析前處理流程 16
3.3 以四種模擬信號分析EEMD解構特性 17
3.3 應用EEMD分析法使得fMRI信號降噪 19
3.4 fMRI信號之希爾伯特頻譜(Hilbert Spectrum) 23
3.5 應用GPU平行處理加速EEMD運算速度 24
第四章結果 25
4.1 EEMD解構四種模擬信號特性之結果 25
4.2 EEMD去除fMRI雜訊後活化反應之比較 31
4.3 fMRI訊號之希爾伯特動態頻譜分析 36
4.4以GPU平行處理提升EEMD運算速度之結果 38
第五章討論 40
5.1 利用HHT分析探討fMRI中非穩態特性對於IMF影響力 40
5.2 由fMRI全腦T值分佈探討EEMD去雜訊方法合理性 44
5.4 由task fMRI訊號之頻譜觀察執行認知作業頻率轉變過程 45
5.5 對於不同方法取得fMRI希爾伯特頻譜的差異性 48
5.6 利用GPU加速對於高迭代次數的fMRI資料型態 50
第六章結論與未來展望 51
參考文獻 52
附錄 55
附錄1.使用SPM8分析EEMD資料所需調整項目 55
附錄2.EEMD分析fMRI資料參數調整以及分析程式碼架構說明 55
附錄3.使用Hilbert時頻分析fMRI資料參數調整 56

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指導教授

吳昌衛

審核日期

2013-8-27

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