Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Chan, DY; Morris, DC; Polascik, TJ; Palmeri, ML; Nightingale, KR

Published Date

  • July 2021

Published In

Volume / Issue

  • 68 / 7

Start / End Page

  • 2472 - 2481

PubMed ID

  • 33760733

Pubmed Central ID

  • PMC8363049

Electronic International Standard Serial Number (EISSN)

  • 1525-8955

Digital Object Identifier (DOI)

  • 10.1109/TUFFC.2021.3068377


  • eng

Conference Location

  • United States