摘要
傳統基於逆投影演算法光聲斷層掃描影像重建會受到有限孔徑效應影響。有限孔徑效應來自於探頭孔徑大小造成的空間脈衝響應，導致切向解析度變差。在傳統上的解決方法是使用聚焦式探頭，利用探頭聚焦點作為虛擬點探頭來降低有限孔徑效應對影像的影響。在本研究中我們提出模型式光聲斷層掃描影像重建法，針對光聲斷層掃描成像系統建立數學模型，基於還原觀測區域的光吸收分佈推導出最佳的濾波器，消除有限孔徑造成的空間脈衝響應。在模擬與實驗驗證上我們比較了模型式光聲影像重建法(model-based image reconstruction)與逆投影演算法(back-projection algorithm)。模擬結果顯示不管使用聚焦式探頭或是非聚焦式探頭兩種方法的徑向解析度皆為最佳。切向解析度則是模型式光聲影像重建法可維持均勻的解析度，但是逆投影演算法離掃描圓心越遠解析度越差。此外，仿體實驗結果亦與模擬結果相匹配。因為使用模型式光聲影像重建法需要使用到大量記憶體，所以實際運用上包含許多限制導致實用性不佳，為了配合處理實際實驗數據，我們提出化簡模型式光聲影像重建法中矩陣運算複雜度的方法，可以根據實驗數據做彈性調整和加大重建影像的觀察區域。未來我們希望更進一步模型式光聲影像重建法，達到減少計算複雜度和運算時間，增加此演算法的實用性。

Abstract
Image reconstruction for photoacoustic tomography is affected by the finite aperture effect. The finite-aperture effect is caused by spatial impulse response associated with finite-sized acoustic transducer, resulting in the worse tangential resolution when imaging points are close to the transducer position. In this study, we developed a model-based image reconstruction technique for photoacoustic tomography to solve such a problem. Based on a linear, discrete PAT imaging model, the proposed method employs a spatiotemporal optimal filter designed in minimum mean square error sense to compensate the SIRs associated with an unfocused and focused transducer at every imaging point; thus retrospective restoration of the tangential resolution can be achieved. Simulation and experimental results demonstrate that this method can substantially improve the degraded tangential resolution for PAT with finite-sized unfocused transducers while retaining the radial resolution. In actually, doing model-based image reconstruction contains many limitations leading to low efficiency, because it requires a lot amount of memories. For cooperating to deal with experimental data, we propose a method to simplify complexity of the algorithm of model-based image reconstruction. It would be adjusted from the condition of experiment and increases the range of observation. In future, We hope to further improve model-based image reconstruction to reduce the computational complexity. It increases the algorithm's usefulness.

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