Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning
This work addresses the need for more accurate and fast local elasticity estimation in medical imaging, particularly for tissue characterization, though it is incremental as it builds on existing deep learning and ultrasound techniques.
The paper tackled the problem of estimating tissue elasticity in ultrasound shear wave imaging by using 3D spatio-temporal CNNs to analyze local shear wave propagation, achieving a mean absolute error of 5.01±4.37 kPa and reducing errors by up to 85.24% compared to conventional methods.
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+-4.37 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.