  Photoacoustic tomography (PAT) is a promising imaging technique that can
visualize the distribution of chromophores within biological tissue. However,
the accuracy of PAT imaging is compromised by light fluence (LF), which hinders
the quantification of light absorbers. Currently, model-based iterative methods
are used for LF correction, but they require significant computational
resources due to repeated LF estimation based on differential light transport
models. To improve LF correction efficiency, we propose to use Fourier neural
operator (FNO), a neural network specially designed for solving differential
equations, to learn the forward projection of light transport in PAT. Trained
using paired finite-element-based LF simulation data, our FNO model replaces
the traditional computational heavy LF estimator during iterative correction,
such that the correction procedure is significantly accelerated. Simulation and
experimental results demonstrate that our method achieves comparable LF
correction quality to traditional iterative methods while reducing the
correction time by over 30 times.
