Options
Do we need pre-processing for deep learning based ultrasound shear wave elastography?
Citation Link: https://doi.org/10.15480/882.16567
Publikationstyp
Journal Article
Date Issued
2026-01-20
Sprache
English
Author(s)
TORE-DOI
Volume
11
Issue
2
Start Page
68
End Page
71
Citation
Current Directions in Biomedical Engineering 11 (2): 68-71 (2025)
Publisher DOI
Publisher
Walter de Gruyter GmbH
Estimating the elasticity of soft tissue can provide useful information for various diagnostic applications. Ultrasound shear wave elastography offers a non-invasive approach. However, its generalizability and standardization across different systems and processing pipelines remain limited. Considering the influence of image processing on ultrasound based diagnostics, recent literature has discussed the impact of different image processing steps on reliable and reproducible elasticity analysis. In this work, we investigate the need of ultrasound pre-processing steps for deep learning-based ultrasound shear wave elastography. We evaluate the performance of a 3D convolutional neural network in predicting shear wave velocities from spatio-temporal ultrasound images, studying different degrees of pre-processing on the input images, ranging from fully beamformed and filtered ultrasound images to raw radiofrequency data. We compare the predictions from our deep learning approach to a conventional timeof- flight method across four gelatin phantoms with different elasticity levels. Our results demonstrate statistically significant differences in the predicted shear wave velocity among all elasticity groups, regardless of the degree of pre-processing. Although pre-processing slightly improves performance metrics, our results show that the deep learning approach can reliably differentiate between elasticity groups using raw, unprocessed radiofrequency data. These results show that deep learning-based approaches could reduce the need and the bias of traditional ultrasound pre-processing steps in ultrasound shear wave elastography, enabling faster and more reliable clinical elasticity assessments.
DDC Class
616.07: Pathology
621.3: Electrical Engineering, Electronic Engineering
006.31: Machine Learning
Publication version
publishedVersion
Loading...
Name
10.1515_cdbme-2025-0318-1.pdf
Type
Main Article
Size
1.79 MB
Format
Adobe PDF