Zengqiu Yuchen, Wu Wentao, Xiao Ling, Zhou Erlei, Cao Zheng, Hua Jiadong, Wang Yue
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2024 Mar 15;24(6):1895. doi: 10.3390/s24061895.
Acoustic aberration, caused by the uneven distribution of tissue speed-of-sound (SoS), significantly reduces the quality of ultrasound imaging. An important approach to mitigate this issue is imaging correction based on local SoS estimation. Computed ultrasound tomography in echo mode (CUTE) is an SoS estimation method that utilizes phase-shift information from ultrasound pulse-echo signals, offering both practical utility and computational efficiency. However, the traditional single-pass CUTE often suffers from poor accuracy and robustness. In this paper, an advanced approach known as iterative CUTE is introduced, which refines SoS estimates through iterative correction of errors and noise, addressing the limitations of traditional single-pass methods. It is argued that traditional precision indicators like root mean square error (RMSE) fall short in adequately reflecting the quality of SoS estimates for imaging correction, and coherence factor (CF) is proposed as a more indicative metric. Performance validation of the iterative CUTE algorithm was conducted using a simulation and agar phantom experiment. The results indicated that the iterative CUTE approach surpasses the single-pass approach, enhancing the average CF for SoS estimates by up to 18.2%. In phantom experiments, imaging corrected with SoS estimates from iterative CUTE reduced the Array Performance Index (API) by up to 40% compared to traditional methods.
由组织声速（SoS）分布不均引起的声学像差会显著降低超声成像质量。缓解这一问题的一个重要方法是基于局部SoS估计的成像校正。回波模式下的计算机超声断层成像（CUTE）是一种利用超声脉冲回波信号中的相移信息的SoS估计方法，兼具实用性和计算效率。然而，传统的单通道CUTE往往存在精度和稳健性较差的问题。本文介绍了一种称为迭代CUTE的先进方法，该方法通过对误差和噪声进行迭代校正来优化SoS估计，解决了传统单通道方法的局限性。有人认为，像均方根误差（RMSE）这样的传统精度指标不足以充分反映用于成像校正的SoS估计的质量，因此提出相干因子（CF）作为一个更具指示性的指标。使用模拟和琼脂体模实验对迭代CUTE算法进行了性能验证。结果表明，迭代CUTE方法优于单通道方法，将SoS估计的平均CF提高了多达18.2%。在体模实验中，与传统方法相比，用迭代CUTE的SoS估计进行成像校正可使阵列性能指数（API）降低多达40%。
