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  4. Ultrasound shear wave elasticity imaging with spatio-temporal deep learning
 
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Ultrasound shear wave elasticity imaging with spatio-temporal deep learning

Citation Link: https://doi.org/10.15480/882.4723
Publikationstyp
Journal Article
Date Issued
2022-04-19
Sprache
English
Author(s)
Neidhardt, Maximilian  
Bengs, Marcel  
Latus, Sarah  orcid-logo
Gerlach, Stefan  orcid-logo
Cyron, Christian J.  
Sprenger, Johanna  
Schlaefer, Alexander  
Institut
Medizintechnische und Intelligente Systeme E-1  
Kontinuums- und Werkstoffmechanik M-15  
TORE-DOI
10.15480/882.4723
TORE-URI
http://hdl.handle.net/11420/13998
Journal
IEEE transactions on biomedical engineering  
Volume
69
Issue
11
Start Page
3356
End Page
3364
Citation
IEEE Transactions on Biomedical Engineering 69 (11): 3356-3364 (2022-11-01)
Publisher DOI
10.1109/TBME.2022.3168566
Scopus ID
2-s2.0-85128605636
PubMed ID
35439123
Publisher
IEEE
Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01(437) kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.
Subjects
3D deep learning
Elasticity imaging
high-speed ultrasound imaging
soft tissue
spatio-temporal data
MLE@TUHH
DDC Class
600: Technik
610: Medizin
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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