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Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.

Publication ,  Journal Article
Chan, DY; Morris, DC; Polascik, TJ; Palmeri, ML; Nightingale, KR
Published in: IEEE Trans Ultrason Ferroelectr Freq Control
July 2021

Ultrasound elasticity imaging in soft tissue with acoustic radiation force requires the estimation of displacements, typically on the order of several microns, from serially acquired raw data A-lines. In this work, we implement a fully convolutional neural network (CNN) for ultrasound displacement estimation. We present a novel method for generating ultrasound training data, in which synthetic 3-D displacement volumes with a combination of randomly seeded ellipsoids are created and used to displace scatterers, from which simulated ultrasonic imaging is performed using Field II. Network performance was tested on these virtual displacement volumes, as well as an experimental ARFI phantom data set and a human in vivo prostate ARFI data set. In the simulated data, the proposed neural network performed comparably to Loupas's algorithm, a conventional phase-based displacement estimation algorithm; the rms error was [Formula: see text] for the CNN and 0.73 [Formula: see text] for Loupas. Similarly, in the phantom data, the contrast-to-noise ratio (CNR) of a stiff inclusion was 2.27 for the CNN-estimated image and 2.21 for the Loupas-estimated image. Applying the trained network to in vivo data enabled the visualization of prostate cancer and prostate anatomy. The proposed training method provided 26 000 training cases, which allowed robust network training. The CNN had a computation time that was comparable to Loupas's algorithm; further refinements to the network architecture may provide an improvement in the computation time. We conclude that deep neural network-based displacement estimation from ultrasonic data is feasible, providing comparable performance with respect to both accuracy and speed compared to current standard time-delay estimation approaches.

Duke Scholars

Published In

IEEE Trans Ultrason Ferroelectr Freq Control

DOI

EISSN

1525-8955

Publication Date

July 2021

Volume

68

Issue

7

Start / End Page

2472 / 2481

Location

United States

Related Subject Headings

  • Ultrasonography
  • Phantoms, Imaging
  • Neural Networks, Computer
  • Male
  • Humans
  • Elasticity Imaging Techniques
  • Algorithms
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering
 

Citation

APA
Chicago
ICMJE
MLA
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Chan, D. Y., Morris, D. C., Polascik, T. J., Palmeri, M. L., & Nightingale, K. R. (2021). Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging. IEEE Trans Ultrason Ferroelectr Freq Control, 68(7), 2472–2481. https://doi.org/10.1109/TUFFC.2021.3068377
Chan, Derek Y., D Cody Morris, Thomas J. Polascik, Mark L. Palmeri, and Kathryn R. Nightingale. “Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.IEEE Trans Ultrason Ferroelectr Freq Control 68, no. 7 (July 2021): 2472–81. https://doi.org/10.1109/TUFFC.2021.3068377.
Chan DY, Morris DC, Polascik TJ, Palmeri ML, Nightingale KR. Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2021 Jul;68(7):2472–81.
Chan, Derek Y., et al. “Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.IEEE Trans Ultrason Ferroelectr Freq Control, vol. 68, no. 7, July 2021, pp. 2472–81. Pubmed, doi:10.1109/TUFFC.2021.3068377.
Chan DY, Morris DC, Polascik TJ, Palmeri ML, Nightingale KR. Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2021 Jul;68(7):2472–2481.

Published In

IEEE Trans Ultrason Ferroelectr Freq Control

DOI

EISSN

1525-8955

Publication Date

July 2021

Volume

68

Issue

7

Start / End Page

2472 / 2481

Location

United States

Related Subject Headings

  • Ultrasonography
  • Phantoms, Imaging
  • Neural Networks, Computer
  • Male
  • Humans
  • Elasticity Imaging Techniques
  • Algorithms
  • Acoustics
  • 51 Physical sciences
  • 40 Engineering