Ultrasound compound imaging weighted by magnitude squared coherence factor
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摘要:
超声复合成像采用多个发射角度的成像结果直接叠加的方法进行成像, 成像质量较差。基于相干系数的自适应加权方法根据全部发射角度下的成像向量全局特征计算加权系数, 加权复合成像结果来改善分辨率和对比度, 但未改善背景组织的成像质量。为此提出了幅值平方相干系数加权方法, 该方法根据相邻发射角度成像向量频谱之间的相干性确定加权系数, 并对复合成像结果进行加权输出。仿真和体模实验结果表明与相干系数、广义相干系数和环形统计矢量等加权方法相比, 所提方法的背景组织成像结果更加平滑, 具有更好噪声对比度和背景信噪比, 其中背景信噪比分别提高了91.6%, 45.9%和52.3%。相对于未加权的方法, 对比度提高了69.8%。人体实验结果进一步证明了所提方法的有效性, 其中对比度提高了15.8%、9.3%和26.4%, 噪声对比度提高了36.3%、24.8%和32.4%, 背景信噪比分别提高了80%、66.3%和44.3%。
Abstract:The compound imaging method uses the direct superposition of multiple emission imaging results for imaging, which is simple to calculate but has poor imaging quality. The adaptive weighting method based on coherence factor calculates the weighting factor according to the global characteristics of the imaging vectors at all emission angles, which leads to the degradation of the background imaging quality. The paper proposes a magnitude squared coherence factor (MSF) weighting method. This method determines the weighting coefficient based on the coherence between spectrum of the imaging vectors of adjacent emission angles, and performs weighted output on the compound imaging results. The results of the body-mode experiments show that the MSF method results in smoother background and improves the noise contrast and speckle signal-to-noise ratio compared to the conventional weighting methods of coherence coefficient, general coherence factor, and circular statistics vector, in which the speckle signal-to-noise ratio is improved by 91.6%, 45.9%, and 52.3%, respectively. The contrast ratio was improved by 69.8% relative to the unweighted method. The human experimental results further demonstrate the effectiveness of the proposed method. The human experimental results further demonstrate the effectiveness of the proposed method, in which the contrast radio was improved by 15.8%, 9.3%, and 26.4%, the contrast-to-noise ratio by 36.3%, 24.8%, and 32.4%, and the speckle signal-to-noise ratio by 80%, 66.3%, and 44.3%, respectively.
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表 1 不同算法仿真点的横向FWHM及仿真斑的CR, CNR和SSNR
算法 FWHM(mm) CR(dB) CNR SSNR CPWC 0.57 31.94\25.20\17.00 4.55\3.59\2.43 8.44\7.38\6.14 CF 0.44 51.28\45.26\35.06 4.36\3.82\2.63 4.18\3.52\2.13 GCF 0.51 53.89\46.87\35.99 5.34\4.49\3.01 5.45\4.33\2.59 CSV 0.45 47.25\39.34\28.05 4.62\3.68\2.47 4.90\4.08\2.78 MSF 0.57 42.20\47.43\35.41 4.46\5.00\3.05 6.90\5.69\2.84 
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表 2 不同算法实验点的横向FWHM及实验斑的CR, CNR和背景SNR
算法 FWHM(mm) CR(dB) CNR SSNR CPWC 0.56 26.05 3.65 7.35 CF 0.45 44.50 3.66 3.25 GCF 0.51 46.61 4.48 4.27 CSV 0.45 39.89 3.74 4.09 MSF 0.56 44.24 4.81 6.23
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表 3 人体成像数据的CR、CNR和背景SNR
算法 CR(dB) CNR SSNR CPWC 29.31 2.54 3.57 CF 42.21 2.40 1.70 GCF 44.69 2.62 1.84 CSV 38.66 2.47 2.12 MSF 48.88 3.27 3.06
下载: 导出CSV
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