Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Strain Estimation and Frame Selection in Ultrasound Elastography
  using Machine Learning   Ultrasound Elastography aims to determine the mechanical properties of the
tissue by monitoring tissue deformation due to internal or external forces.
Tissue deformations are estimated from ultrasound radio frequency (RF) signals
and are often referred to as time delay estimation (TDE). Given two RF frames
I1 and I2, we can compute a displacement image which shows the change in the
position of each sample in I1 to a new position in I2. Two important challenges
in TDE include high computational complexity and the difficulty in choosing
suitable RF frames. Selecting suitable frames is of high importance because
many pairs of RF frames either do not have acceptable deformation for
extracting informative strain images or are decorrelated and deformation cannot
be reliably estimated. Herein, we introduce a method that learns 12
displacement modes in quasi-static elastography by performing Principal
Component Analysis (PCA) on displacement fields of a large training database.
In the inference stage, we use dynamic programming (DP) to compute an initial
displacement estimate of around 1% of the samples, and then decompose this
sparse displacement into a linear combination of the 12 displacement modes. Our
method assumes that the displacement of the whole image could also be described
by this linear combination of principal components. We then use the GLobal
Ultrasound Elastography (GLUE) method to fine-tune the result yielding the
exact displacement image. Our method, which we call PCA-GLUE, is more than 10
times faster than DP in calculating the initial displacement map while giving
the same result. Our second contribution in this paper is determining the
suitability of the frame pair I1 and I2 for strain estimation, which we achieve
by using the weight vector that we calculated for PCA-GLUE as an input to a
multi-layer perceptron (MLP) classifier.

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The categories of this paper are: q-bio
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The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga