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.
翻译:超声剪动弹性成像是量化组织弹性特性的宝贵工具。 典型地, 剪动波速度的测算和映射为弹性值, 它忽略了传播剪动波或推动序列特性的形状等信息。 我们用超声波数据为本地快速弹性估测提供了 3D spatio- 时空CNN 。 这个方法基于从小地方地区剪动波传播中提取弹性特性的精确估计。 一个大型的培训数据集是用一个机器人获得的, 机器人来自同质 gelatin 象形体, 范围从17.42 kPa 到 126.05 kPa 不等。 结果显示, 我们的方法可以在像素的基础上估计弹性特性特性, 其平均绝对误差为 5.01+-4.37 kPa。 此外, 我们估计当地弹性度, 与推动位置无关, 甚至可以在推动区域内进行准确估测。 对于嵌入的象, 我们报告说, 低53.93% MAE (7. 50 kPa) 至 126. kPa) 的机器人, 以当地常规波数 方法的背景为85.24% 。