Using Spatial Diffusions for Optoacoustic Tomography Image Reconstruction

TL;DR

This paper introduces a novel optoacoustic tomography image reconstruction method that leverages conditional diffusion models and autoencoders to improve image quality, artifact correction, and detail recovery.

Contribution

It proposes a new scheme combining initial reconstruction, autoencoder-based latent representation, and conditional diffusion processes for enhanced image reconstruction.

Findings

Improved PSNR and SSIM metrics demonstrate better image quality.

The method effectively corrects artifacts and recovers finer details.

Numerical results validate the superiority over traditional reconstruction methods.

Abstract

Optoacoustic tomography image reconstruction has been a problem of interest in recent years. By exploiting the exceptional generative power of the recently proposed diffusion models we consider a scheme which is based on a conditional diffusion process. Using a simple initial image reconstruction method such as Delay and Sum, we consider a specially designed autoencoder architecture which generates a latent representation which is used as conditional information in the generative diffusion process. Numerical results show the merits of our proposal in terms of quality metrics such as PSNR and SSIM, showing that the conditional information generated in terms of the initial reconstructed image is able to bias the generative process of the diffusion model in order to enhance the image, correct artifacts and even recover some finer details that the initial reconstruction method is not able to obtain.