Improved Limited-View Ultrasound Tomography via Machine Learning

Tomographic reconstruction is used extensively in medicine, nondestructive testing (NDT), and geology. In an ideal situation, where measurements are taken at all angles around an object, known as full-view configuration, a full reconstruction of the object can be produced. One of the major issues faced in tomographic imaging is when measurements cannot be taken freely around the object under inspection. This may be caused by the size and geometry of the object or difficulty accessing from particular directions. The resulting limited view (LV) transducer configuration leads to a large deterioration in image quality; thus, it is very beneficial to employ a compensation algorithm. At present, the most effective compensation algorithms require a large amount of computing power or a bespoke case-by-case approach, often with numerous arbitrary constants which must be tuned for a specific application. This work proposes a machine learning (ML)-based approach to perform the LV compensation. The model is based around an autoencoder (AE) architecture. It is trained on an artificial dataset, taking advantage of the ability to generate arbitrary LV images given a full view input. The approach is evaluated on ten laser-scanned corrosion maps and the results compared to positivity regularization-a LV compensation algorithm similar in the speed of execution and generalization potential. The algorithms are compared for root mean squared error (RMSE) across the image and maximum absolute error (MAE). Furthermore, they are visually compared for subjective quality. Compared to the conventional algorithm, the ML-based approach improves on the MAE in eight out of the ten cases. The conventional approach performs better on mean RMSE, which is explained by the ML outputting an inaccurate background level, which is not a critical ability. Most importantly, the visual inspection of outputs shows the ML approach reconstructs the images better, especially in the case of irregular corrosion patches. Compared to LV images, the ML method improves both the RMSE and MAE by 41%.