Noise reduction technique using deep learning for ultrasound imaging during high-intensity focused ultrasound treatment

Abstract

One of the problems with ultrasound imaging during high-intensity focused ultrasound (HIFU) treatment is that the therapeutic ultrasound components interfere with the diagnostic ultrasound components, making it impossible to monitor the tissue changes during HIFU exposure. In this study, a convolutional neural network (CNN) framework was applied to the reconstructed ultrasound images with HIFU noise to remove the therapeutic ultrasound components while the diagnostic ultrasound components remain intact. In the experiments, the chicken breast was used as a tissue sample and exposed to HIFU in the water tank. The ultrasound images with and without noise were acquired during an intermission period of HIFU exposure and the noise-reduced images was predicted using the proposed multi-layer regression CNN model through the training process. As a result, ultrasound images with sufficient spatial resolution to detect the thermal lesion were acquired.