  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.
