Least-squares estimation of imaging parameters for an ultrasonic array using known geometric image features

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 least-squares 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.