We study the use of a learned forward operator in the inverse problem of photoacoustic tomography. The Fourier neural operator to approximate the photoacoustic wave propagation is used. Further, the inverse problem is solved using a gradient-based approach with automatic differentiation. The methodology is evaluated using numerical simulations, and the results are compared to a conventional approach, where the forward operator is approximated using the pseudospectral k-space method. The results show that the learned forward operator can be used to approximate the photoacoustic wave propagation with good accuracy, and that it can be utilised as a computationally efficient forward operator in solving the inverse problem of photoacoustic tomography.
Cite
@article{arxiv.2603.21655,
title = {Utilising a learned forward operator in the inverse problem of photoacoustic tomography},
author = {Karoliina Puronhaara and Teemu Sahlström and Andreas Hauptmann and Tanja Tarvainen},
journal= {arXiv preprint arXiv:2603.21655},
year = {2026}
}