基於MVDR與同調權重之可適性光聲陣列造影技術

Adaptive Photoacoustic Array Imaging Based on MVDR and Coherence Weighting

鄭以成(I-Cheng Cheng)

指導教授 ： 李夢麟

國立清華大學/電機資訊學院/電機工程學系/碩士(2011年)

https://doi.org/10.6843/NTHU.2011.00572

摘要

光聲影像擁有聲學的高空間解析度與光學的高對比解晰度，因此在醫學影像上有發展潛力，因為激發光聲訊號之雷射光源在組織內無法聚焦，導致光聲影像的空間解晰度與對比度皆不如超音波影像。傳統的方法通常使用smoothing apodization來解決對比不佳的問題，但卻犧牲解析度。本研究提出一可適性光聲陣列成像方法 – MVDR+CFMV，此方法以最小變異無失真響應波束成形演算法 minimum-variance-distortion response（MVDR）作準確的空間濾波，壓低干擾與雜訊並縮小主波瓣（mainlobe）寬，進一步結合同調權重CF (Coherence Factor weighting)，壓低旁波瓣（sidelobe）功率，達到兼顧解析度和對比的目的。經電腦模擬驗證，於實驗所使用的陣列探頭參數下，MVDR演算法最佳的子陣列長度L約為探頭全長的三分之一，本研究使用128個換能元件的陣列探頭，L值選用48。另外討論演算法使用限制，MVDR+CFMV具有較佳的聲速誤差容忍度約±50m/s，同調權重適合低環境SNR運作，MVDR則需要較高的SNR。經實驗證明，使用MVDR+CFMV可以比傳統的Delay and weighted sum波束合成技術加強壓抑60dB的旁波瓣強度，縮小主波瓣寬 80%，增加24dB的對比度，MVDR+CFMV可以有效的改善光聲影像品質。

關鍵字

可適性波束合成 ； MVDR ； 同調權重 ； 光聲影像

並列摘要

In this study, we attempt to apply adaptive photoacoustic array imaging technique to improve spatial resolution and image contrast in photoacoustic imaging. Due to the diffusive nature of the photoacoustic excitation laser source in biological tissue, the spatial resolution and image contrast of photoacoustic imaging are worse than those of ultrasound imaging. Conventionally, apodization is commonly employed to enhance image contrast while the spatial resolution is sacrificed. To solve such issues, we proposed an adaptive photoacoustic array imaging technique based on a MVDR algorithm along with coherence-factor (CF) weighting. The MVDR algorithm suppresses sidelobe interferences and noises while offering narrower mainlobe width. That is, higher spatial resolution can be obtained. In addition, based on the MVDR algorithm, a MVDR+CFMV weighting technique is derived and applied to further suppress sidelobes; thus higher spatial resolution and better image contrast can be obtained simultaneously. Simulation results showed that the sub-array length of the MVDR algorithm is about one third of full aperture length. In this study, we used a 128-element array transducer, therefore the employed optimal sub-array length is 48. Additionally, we discuss the algorithm limited. Simulation results showed that MVDR+CFMV the Velocity error tolerance is about ±50m/s which is better than other algorithm. CF weighting can work in low SNR environment, but MVDR need high SNR. Experimental results demonstrated that our proposed MVDR+CFMV weighting can suppress sidelobes by 60dB, reduce mainlobe width by 80% , improve contrast resolution by 24dB compared with conventional delay and sum beamforming; showing the efficacy of our proposed method.

參考文獻

1. Bell, A.G., Upon the production and reproduction of sound by light. Telegraph Engineers, Journal of the Society of, 1880. 9(34): p. 404-426.

2. Wang, L.V., Tutorial on photoacoustic microscopy and computed tomography. IEEE Journal of Selected Topics in Quantum Electronics, 2008. 14(1): p. 171-179.

4. Ku, G. and L.H.V. Wang, Deeply penetrating photoacoustic tomography in biological tissues enhanced with an optical contrast agent. OPTICS LETTERS, 2005. 30(5): p. 507-509.

5. Ku, G., et al., Deep penetrating photoacoustic tomography in biological tissues. Photons Plus Ultrasound: Imaging and Sensing 2005, 2005. 5697: p. 117-126.

7. Capon, J., HIGH-RESOLUTION FREQUENCY-WAVENUMBER SPECTRUM ANALYSIS OF LASA DATA. Transactions-American Geophysical Union, 1969. 50(4): p. 244-&.