Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.
Convolutional neural network
Deep learning
Particle velocity estimation
Shear wave elastography
Ultrasound
Journal
Ultrasound in medicine & biology
ISSN: 1879-291X
Titre abrégé: Ultrasound Med Biol
Pays: England
ID NLM: 0410553
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
received:
12
08
2022
revised:
01
02
2023
accepted:
07
02
2023
medline:
17
5
2023
pubmed:
24
4
2023
entrez:
23
04
2023
Statut:
ppublish
Résumé
Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity. In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity V By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas' autocorrelation algorithm with an improved SNR of 4.47 dB for the V The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes.
Identifiants
pubmed: 37088606
pii: S0301-5629(23)00055-8
doi: 10.1016/j.ultrasmedbio.2023.02.004
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1518-1526Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.