大多數的高頻線性陣列探頭使用的探頭陣元間距大於發射波長的一半。傳統影像重建法無可避免地會受到柵瓣及旁瓣假影的影響。旁瓣假影形成的原因是陣元寬度影響計算時間延遲的準確度。柵瓣假影則是線性陣列陣元間距大於半波長而造成的。為了消弭柵瓣以及旁瓣假影的影響，我們提出了基於虛擬接收元素及衍生相關權重的成像方法。各個探頭陣元會被視為數個虛擬探頭陣元，而虛擬探頭陣元使用原探頭陣元的訊號進行成像。挑選數個合適的虛擬探頭陣元組合重建出多張影像，然後再疊加這些影像。由於這些影像擁有相同的主訊號分布及不同的假影分布，因此再疊加這些影像之後可以達到抑制假影的效果。為了更進一步抑制假影，透過計算這些影像的相關係數當作加權的參數。如此一來，主訊號會被保留，而假影則會被抑制。在模擬結果驗證了此成像方法的等效公式。此外，藉由線及細管仿體實驗來進行驗證。在平面波成像下，旁瓣以及柵瓣可分別被抑制1.17 dB及4.82 dB。利用權重參數，旁瓣以及柵瓣可更進一步被抑制1.6 dB及3.56 dB。而在光聲成像下，旁瓣以及柵瓣可分別被抑制2.54 dB及5.54 dB。利用權重參數，旁瓣以及柵瓣可更進一步被抑制1.57 dB及4.4 dB。

The high-frequency linear array transducers which array pitch is larger than one half wavelength are generally used for the plane-wave imaging and the photoacoustic linear array imaging. It inevitably suffers the side-lobe and the grating-lobe artifacts when the delay-and-sum (DAS) beamformation is used for the image reconstruction. The side-lobe artifacts are caused by the larger element width which reduces delay accuracy, and the grating-lobe artifacts are caused by the array pitch which is greater than one half wavelength. In order to suppress both of artifacts, we propose a novel beamforming method based on a concept of virtual sub-wavelength receiving elements and demonstrate the corresponding mathematical modeling. Each array element is equally divided into multiple virtual sub-elements. The signals of the virtual sub-elements are synthesized using the signal received by their corresponding physical element. DAS on receive is done with the synthesized signals and corresponding delays of the virtual sub-elements. With the proper grouping of the virtual receiving elements, images formed with different groups of the virtual elements own similar main-lobe signals and different grating-lobe and side-lobe artifacts. Then, several images are accumulated to achieve artifact suppression because several images have same main-lobe distribution and different artifact distribution. In addition, to further suppress grating-lobe and side-lobe artifacts, virtual-receiving-element derived correlation weighting can be performed. Main-lobe signals can be distinguished from grating-lobe and side-lobe clutters using correlation among the beamformed RF data from different groups of the virtual elements. The correlation is then used as a pixel-by-pixel weighting factor to the image formed by all the virtual receiving elements, which retains the main-lobe signals and further suppresses the grating-lobe and side-lobe contributions. Simulation results demonstrate the mathematical modeling of the proposed method. In addition, the wire phantom and tube experiments using a research ultrasound engine were performed to verify our idea. Our method suppressed 1.17 dB for side-lobes and 4.82 dB for grating-lobes in the plane-wave ultrasound imaging. Side-lobes and grating-lobes can be further suppressed 1.6 dB and 3.56 dB after applying the weighting function. Our method suppressed 2.54 dB for side-lobes and 5.54 dB for grating-lobes in the photoacoustic imaging. Side-lobes and grating-lobes can be further suppressed 1.57 dB and 4.4 dB after applying the weighting function.

中文摘要 I
Abstract II
Contents IV
List of Figures VII
List of Tables XIII
Chapter 1 Introduction 1
1.1 Plane Wave Based Ultrasound Imaging Using Linear Array 1
1.2 Photoacoustic Imaging Using Linear Array 4
1.3 Artifacts of Linear Array Imaging 6
1.3.1 Side-Lobe Artifacts 6
1.3.2 Grating-Lobe Artifacts 8
1.4 Common Methods for Artifacts Suppression 10
1.4.1 Method to Suppress Side-lobe Artifacts 10
1.4.2 Method to Suppress Grating-lobe Artifacts 11
1.4.3 Motivation 13
Chapter 2 Materials and Methods 14
2.1 Hypothesis 14
2.2 Virtual Element and Imaging Model 16
2.3 Virtual Element and Derived Weighting Function 20
2.3.1 Two Virtual Elements per Element 20
2.3.2 Over Two Virtual Elements per Element 21
Chapter 3 Results and Discussion 23
3.1 Simulations 23
3.1.1 Verification of Modeling 23
3.1.2 Multiple Point Targets 27
3.1.3 Different Depths 29
3.1.4 Different Center Frequencies 32
3.1.5 Different Bandwidths 38
3.1.6 F-number 41
3.1.7 Position of the virtual elements 45
3.1.8 Other Methods 49
3.1.9 Derived Weighting Function 51
3.2 Experiments 55
3.2.1 Ultrasound 55
3.2.2 Photoacoustic imaging 62
Chapter 4 Conclusions and Future Work 74
4.1 Conclusions 74
4.2 Future Work 75
References 76

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