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Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.
- Source :
-
Ultrasound in Medicine & Biology . Jul2023, Vol. 49 Issue 7, p1518-1526. 9p. - Publication Year :
- 2023
-
Abstract
- Objective: 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. Methods: In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity V z. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances. Discussion: 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 z signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data. Conclusion: The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03015629
- Volume :
- 49
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Ultrasound in Medicine & Biology
- Publication Type :
- Academic Journal
- Accession number :
- 163697568
- Full Text :
- https://doi.org/10.1016/j.ultrasmedbio.2023.02.004