光學解析度光聲顯微鏡在橫向上有較高的解析度與安全性，因此被廣泛的使用在功能性影像上，對於醫學研究上也有極大的發展性。近期，微血管內的血流流速估計也引起廣大的興趣。在本文中，根據光聲訊號寬頻且無中心頻率的特性，我們參考超音波以寬頻量測血流流速的蝴蝶演算法，經過修正，提出一套新的方式來量測光聲訊號的流速，可以同時估計軸向與橫向的流速資訊，在考慮雜訊的情況下，可以在光聲訊號的血流資料中找到一條變異數最小的直線，其斜率為軸向的流速。此外，不同於傳統測量流速橫向流速的最大振幅投影方法，透過最佳蝶狀線的資料，進而得到橫向的流速資訊。文中，透過光學解析度光聲訊號的M- mode模擬模型 來驗證我們的演算法，且與過去主要用來估算橫向與軸向的演算法做比較。 可以發現比起傳統的方法，我們的方法在抗雜訊上有較好的表現，實驗結果也顯示，我們所提出的演算法是可行的。

Photoacoustic (PA) microscopy has been widely used in functional imaging. Because of the safety and high resolution, it has great potential for biomedical study. Recently, there is growing interest in flow estimation in vivo down to capillary level. In this study, according to the characteristics of optical resolution photoacoustic microscopy (ORPAM) flow signals, e.g., wideband and no carrier frequency, we propose a novel ORPAM flow estimation algorithm using the butterfly search technique which allows the simultaneous estimation of both axial flow velocity and lateral flow rate. Taking the presence of noise into account, the slope of the butterfly line over the PA flow data set having the minimum variance indicates the axial flow velocity. In addition, instead of the maximum amplitude projection (MAP) of the flow data set, the flow data along the selected butterfly line can be used to estimate lateral flow rate simultaneously. Here, the feasibility of the proposed method is verified via the M-mode simulation based on an OR-PAM photoacoustic signal model, and the performance of the proposed method is compared with that of the conventional single axis flow estimators. It is found that our method shows better noise immunity than the conventional methods. The experimental results also show that our method is feasible.

摘要 I
Abstract I
Table of Contents II
List of Figures………………………………………………………………………...V
List of Tables……………………………………………………………………… ....X
Chapter 1 Introduction 1
1.1 Optical Resolution Photoacoustic Microscopy…………………………...1
1.2 Blood Flow Mearsurement 1
1.3 Conventional Method 2
1.4 Motivation 5
1.5 Composition of the thesis 6
Chapter 2 Materials and Methods 7
2.1 Modified Butterfly Search Algorithm 7
2.2 Axial Flow Estimation 9
2.2.1 Selection of ROI & Line by Line Normalization 10
2.2.2 Initial Guess 11
2.2.3 Butterfly Search 12
2.3 Lateral Flow Estimation 13
2.3.1 Proposed Method 14
2.3.2 Autocorrelation 16
Chapter 3 Results and Discussion 18
3.1 Simulations 18
3.1.1 Laser Beam………………………………………………………...18
3.1.2 N shape…..………………………………………………………...20
3.1.3 Spatial Impulse Response………………………………….……....21
3.1.4 Electrical Impulse Response…………………………….………...21
3.1.5 Hypothesis…………………………………………………………22
3.2 Simulation Results 23
3.3 Experiment 30
3.3.1 System Architectures and Specifications 30
3.3.2 Experimental Results 32
Chapter 4 Conclusions and Future Work 40
4.1 Conclusions 40
4.2 Future Work 41
References 42

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