SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.

ABSTRACT:

Objective

Ultrasound-based shear wave elastography offers estimation of tissue stiffness through analysis of the propagation of a shear wave induced by a stimulus. Displacement or velocity fields during the process can contain noise as a result of the limited number of acquisitions. With advances in physics-informed deep learning, neural networks can approximate a physics field by minimizing the residuals of governing physics equations.

Methods

In this research, we introduce a shear wave elastography Fourier feature network (SELFNet) using spatial-temporal random Fourier features within a physics-informed neural network framework to estimate and denoise particle displacement signals. The network uses a sparse mapping to increase robustness and incorporates the governing equa

SUBMITTER: Xie Y 

PROVIDER: S-EPMC11490379 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature