Preprocessing of Freehand Ultrasound Synthetic Aperture Measurements using DNN

Abstract

Manual ultrasonic inspection is a widely used Nondestructive Testing (NDT) technique due to its simplicity and compatibility with complex structures. However, in contrast to the data acquired using a robotic positioner, manual measurements suffer from perturbations caused by a variable coupling and a varying scanning density. Imaging techniques like the synthetic aperture focusing technique rely on an unperturbed dense measurement from an equidistant measurement grid. Consequently, imaging based on freehand measurements leads to artifacts. This work aims at reducing such artifacts by preprocessing the manual measurements using Deep Neural Networks (DNN). The training of a DNN requires a large set of labeled measurements which is difficult to obtain in NDT. In this work, we present a technique to train the DNN using only synthetic data. We show that the resulting DNN generalizes well on real measurements. We present an improvement in Generalized Contrast to Noise Ratio by a factor of 20 and 3 compared to omitting the preprocessing for synthetic and measurement data, respectively.