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Publisher DOI: 10.1109/TBME.2022.3168566
Title: Ultrasound shear wave elasticity imaging with spatio-temporal deep learning
Language: English
Authors: Neidhardt, Maximilian 
Bengs, Marcel 
Latus, Sarah  
Gerlach, Stefan  
Cyron, Christian J. 
Sprenger, Johanna 
Schlaefer, Alexander 
Keywords: 3D deep learning; Elasticity imaging; high-speed ultrasound imaging; soft tissue; spatio-temporal data
Issue Date: 19-Apr-2022
Publisher: IEEE
Source: IEEE Transactions on Biomedical Engineering 69 (11): 3356-3364 (2022-11-01)
Abstract (english): 
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.
DOI: 10.15480/882.4723
ISSN: 1558-2531
Journal: IEEE transactions on biomedical engineering 
Institute: Medizintechnische und Intelligente Systeme E-1 
Kontinuums- und Werkstoffmechanik M-15 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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