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Generative priors-constraint accelerated iterative reconstruction for extremely sparse photoacoustic tomography boosted by mean-reverting diffusion model: Towards 8 projections

Year: 2025

Type: article

Abstract: An advanced sparse reconstruction method is proposed for extremely sparse photoacoustic tomography based on the mean-reverting diffusion model. By modeling the degradation process from a high-quality image under full-view scanning to a sparse image with stable Gaussian noise, a mean-reverting diffusion model is trained to learn prior information of the data distribution. Then the learned information is employed to generate a high-quality image from the sparse image. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction. The proposed method achieves an improvement of 0.52 (~289 %) in structural similarity and 10.01 dB (~59 %) in peak signal-to-noise ratio for extremely sparse projections (8 projections), compared with the traditional method. This method is expected to shorten the acquisition time and reduce the cost of photoacoustic tomography.

Source: Photoacoustics

DOI: 10.1016/j.pacs.2025.100709