The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflected ultrasound radio-frequency(RF) data obtained by simulation from real CT scans of a human body. We also show that CNN is able to imitate existing computationally heavy despeckling methods, thereby saving orders of magnitude in computations and making them amenable to real-time applications.
@article{arxiv.1710.06304,
title = {Towards CT-quality Ultrasound Imaging using Deep Learning},
author = {Sanketh Vedula and Ortal Senouf and Alex M. Bronstein and Oleg V. Michailovich and Michael Zibulevsky},
journal= {arXiv preprint arXiv:1710.06304},
year = {2017}
}