Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning

TL;DR

This paper introduces a 3D spatio-temporal CNN approach for rapid, pixelwise elasticity estimation in ultrasound shear wave imaging, outperforming traditional methods in accuracy and robustness, especially within local regions and inclusions.

Contribution

The novel deep learning method estimates local tissue elasticity from ultrasound data, capturing shear wave features beyond traditional velocity-based techniques.

Findings

Achieves mean absolute error of 5.01 kPa in elasticity estimation.

Reduces MAE by over 50% in phantom inclusions compared to conventional methods.

Performs accurate elasticity estimation within push regions and independent of push location.

Abstract

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.