Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation

Abstract

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster. Moreover, the FNO network was generalizable and can generate simulations not observed in the training data.

References in corpus (8)

• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
• Solving ill-posed inverse problems using iterative deep neural networks
• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Solving parametric PDE problems with artificial neural networks
• Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
• Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning
• An optimized ultrasound detector for photoacoustic breast tomography
• Joint Reconstruction of Absorbed Optical Energy Density and Sound Speed Distribution in Photoacoustic Computed Tomography: A numerical Investigation