Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards CT-quality Ultrasound Imaging using Deep Learning   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.

26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex