在多角度平面波(PW) 成像中，多角度PW同調複合成像是基於傳統延遲加總成像的作法，因此對影像品質的改善有限。延遲相乘加總成像(Delay Multiply And Sum, DMAS) 透過將空間同調性引入到成像過程來改善影像的解析度與對比度，然而過多的空間同調性會影響斑點品質，導致對比雜訊比下降，本研究把最小方差法(Minimum Variance, MV) 與DMAS結合，目的是提高影像解析度並保持一定的斑點品質。首先，使用PICMUS的模擬資料集確定最佳的MV成像方式後再與DMAS結合，在最後選定的TxMV-DMAS成像中，所有可用的PW角度各自複合低解析度影像以形成發射角度向量，在保持相位的同時對該向量開p次根號，接著估計MV權重後將輸出進行p次方以導入空間同調性提高影像品質。我們還開發了自適應的DMAS成像(Adaptive DMAS, ADMAS)以維持背景斑點品質，ADMAS會基於發射角度向量的空間同調性與變異數選擇每個影像像素的p值。以PICMUS實驗資料集的測試結果顯示TxMV-DMAS成像明顯提高了影像解析度，以p固定為2.5為例，主瓣從多角度PW同調複合成像成像的0.57 mm縮小到0.21 mm而影像對比度也從-27.0 dB提升至-42.1 dB，但對比雜訊比卻從12.8下降到10.9，若使用ADMAS方法則能夠在保持相當影像解析度和對比度的情況下維持對比雜訊比。

For multi-angle plane-wave (PW) imaging, coherent PW compounding (CPWC) method provides limited image quality because of its conventional Delay-and-Sum (DAS) beamforming. Delay-Multiply-and-Sum (DMAS) beamforming has been used to improve image resolution and contrast by introducing signal coherence into the image output. Nonetheless, excessive signal coherence may compromise the speckle quality and thus degrade the contrast-to-noise ratio (CNR). In this study, minimum-variance (MV) estimation is combined with DMAS beamforming to increase the image resolution while the background speckle quality is better maintained. First, the optimal MV estimation method is determined for multi-angle PW imaging and then subsequently integrated with DMAS algorithm. In the proposed TxMV-DMAS beamforming, low-resolution images are firstly constructed for all available PW transmit angles to generate the transmit vector. After magnitude-scaling the transmit vector by p-th root while maintaining the phase, the MV weighting is estimated for each image pixel and the high-resolution image is produced with p-th power of its magnitude to introduce the signal coherence. Moreover, adaptive DMAS (ADMAS) is developed to keep speckle quality. It selects the appropriate p value for each image pixel based on the coherence and variance of transmit vector. Our results show that, using the PICMUS dataset, TxMV-DMAS beamforming significantly improves the image resolution compared to CPWC. When the p value is fixed to 2.5 as an example, the main-lobe width in the experiments reduces from 0.57 mm in CPWC to 0.21 mm in TxMV-DMAS. Meanwhile, the image contrast improves from -27.0 dB in CPWC to -42.1 dB in TxMV-DMAS. Nonetheless, the corresponding CNR decreases from 12.8 to 10.9 due to the degraded speckle quality. With ADMAS algorithm, however, the adaptive p value helps to maintain the CNR with comparable image resolution and contrast.

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