Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthesizing dynamic MRI using long-term recurrent convolutional
  networks   A method is proposed for converting raw ultrasound signals of respiratory
organ motion into high frame rate dynamic MRI using a long-term recurrent
convolutional neural network. Ultrasound signals were acquired using a
single-element transducer, referred to here as `organ-configuration motion'
(OCM) sensor, while sagittal MR images were simultaneously acquired. Both
streams of data were used for training a cascade of convolutional layers, to
extract relevant features from raw ultrasound, followed by a recurrent neural
network, to learn its temporal dynamics. The network was trained with MR images
on the output, and was employed to predict MR images at a temporal resolution
of 100 frames per second, based on ultrasound input alone, without any further
MR scanner input. The method was validated on 7 subjects.

4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph