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Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
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Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.

Xufei Chen1, Nishith Chennakeshava1, Rogier Wildeboer2

  • 1Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Ultrasound in Medicine & Biology
|April 23, 2023
PubMed
Summary

A novel 3-D multi-resolution convolutional neural network (MRCNN) improves shear wave elastography (SWE) by enhancing signal-to-noise ratio (SNR) for more accurate tissue property estimation, especially in low SNR conditions.

Keywords:
Convolutional neural networkDeep learningParticle velocity estimationShear wave elastographyUltrasound

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Area of Science:

  • Medical imaging
  • Biophysics
  • Machine learning

Background:

  • Tissue mechanical properties are crucial for disease detection and staging.
  • Shear wave elastography (SWE) quantifies tissue mechanics using shear wave (SW) particle motion.
  • Enhancing the signal-to-noise ratio (SNR) of SW particle motion improves elasticity and viscosity estimates.

Purpose of the Study:

  • To develop a 3-D multi-resolution convolutional neural network (MRCNN) for improved estimation of SW particle velocity (Vz).
  • To introduce a novel method for generating high-SNR training data from real-world noisy acquisitions.

Main Methods:

  • A 3-D multi-resolution convolutional neural network (MRCNN) was designed to estimate Vz.
  • Training data was generated from real SWE acquisitions, paired with high-SNR ground truth.
  • The MRCNN was validated on in vitro breast phantom and ex vivo liver data.

Main Results:

  • The MRCNN achieved a 4.47 dB SNR improvement for Vz signals compared to Loupas' autocorrelation algorithm.
  • Elasticity estimates showed a two-fold decrease in standard deviation.
  • Elasticity maps exhibited a two-fold increase in contrast-to-noise ratio.

Conclusions:

  • The proposed MRCNN significantly enhances Vz estimation accuracy in SWE.
  • The network demonstrates superior performance over traditional methods, particularly in low SNR environments.
  • This approach offers improved accuracy for quantitative tissue characterization using SWE.