  Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality
that combines optical absorption contrast with ultrasound imaging depth. One
challenge in PAT is image reconstruction with inadequate acoustic signals due
to limited sensor coverage or due to the density of the transducer array. Such
cases call for solving an ill-posed inverse reconstruction problem. In this
work, we use score-based diffusion models to solve the inverse problem of
reconstructing an image from limited PAT measurements. The proposed approach
allows us to incorporate an expressive prior learned by a diffusion model on
simulated vessel structures while still being robust to varying transducer
sparsity conditions.
