Department of Mechanical Engineering, University of Bristol, Bristol, UK.
IEEE Trans Ultrason Ferroelectr Freq Control. 2011 Feb;58(2):414-26. doi: 10.1109/TUFFC.2011.1819.
Ultrasonic array images are adversely affected by errors in the assumed or measured imaging parameters. For non-destructive testing and evaluation, this can result in reduced defect detection and characterization performance. In this paper, an autofocus algorithm is presented for estimating and correcting imaging parameter errors using the collected echo data and a priori knowledge of the image geometry. Focusing is achieved by isolating a known geometric feature in the collected data and then performing a weighted leastsquares minimization of the errors between the data and a feature model, with respect to the unknown parameters. The autofocus algorithm is described for the estimation of element positions in a flexible array coupled to a specimen with an unknown surface profile. Experimental results are shown using a prototype flexible array and it is demonstrated that (for an isolated feature and a well-prescribed feature model) the algorithm is capable of generating autofocused images that are comparable in quality to benchmark images generated using accurately known imaging parameters.
超声阵列图像会受到假设或测量的成像参数中的误差的不利影响。对于无损检测和评估，这可能会导致缺陷检测和特征描述性能降低。在本文中，提出了一种自动聚焦算法，用于使用采集的回波数据和图像几何形状的先验知识来估计和校正成像参数误差。通过在采集的数据中隔离已知的几何特征，并针对未知参数执行数据与特征模型之间的误差的加权最小二乘最小化，实现聚焦。描述了用于估计与具有未知表面轮廓的试件耦合的柔性阵列中的元件位置的自动聚焦算法。使用原型柔性阵列显示了实验结果，并证明（对于孤立的特征和规定良好的特征模型），该算法能够生成与使用准确已知的成像参数生成的基准图像质量相当的自动聚焦图像。
