Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High frame-rate cardiac ultrasound imaging with deep learning   Cardiac ultrasound imaging requires a high frame rate in order to capture
rapid motion. This can be achieved by multi-line acquisition (MLA), where
several narrow-focused received lines are obtained from each wide-focused
transmitted line. This shortens the acquisition time at the expense of
introducing block artifacts. In this paper, we propose a data-driven
learning-based approach to improve the MLA image quality. We train an
end-to-end convolutional neural network on pairs of real ultrasound cardiac
data, acquired through MLA and the corresponding single-line acquisition (SLA).
The network achieves a significant improvement in image quality for both $5-$
and $7-$line MLA resulting in a decorrelation measure similar to that of SLA
while having the frame rate of MLA.

8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat