  In this paper we consider the problem of acoustic inversion in the context of
the optoacoustic tomography image reconstruction problem. By leveraging the
ability of the recently proposed diffusion models for image generative tasks
among others, we devise an image reconstruction architecture based on a
conditional diffusion process. The scheme makes use of an initial image
reconstruction, which is preprocessed by an autoencoder to generate an adequate
representation. This representation is used as conditional information in a
generative diffusion process. Although the computational requirements for
training and implementing the architecture are not low, several design choices
discussed in the work were made to keep them manageable. Numerical results show
that the conditional information allows to properly bias the parameters of the
diffusion model to improve the quality of the initial reconstructed image,
eliminating artifacts or even reconstructing finer details of the ground-truth
image that are not recoverable by the initial image reconstruction method. We
also tested the proposal under experimental conditions and the obtained results
were in line with those corresponding to the numerical simulations.
Improvements in image quality up to 17 % in terms of peak signal-to-noise ratio
were observed.
