Multiple Diffusion Models-Enhanced Extremely Limited-View Reconstruction Strategy for Photoacoustic Tomography Boosted by Multi-Scale Priors

Abstract

Multiple diffusion models-enhanced reconstruction strategy is proposed for limited-view photoacoustic tomography (PAT) reconstruction. Boosted by the multi-scale priors of the sinograms obtained in the full view and the limited-view case of 240°, the alternating iteration method is adopted to generate data for missing views on the sinogram domain. The strategy refines the image information from global to local, which promotes high-quality PAT reconstruction. The blood vessel simulation dataset and the in vivo experimental dataset were utilized to assess the performance of the proposed method. When applied to the in vivo experimental dataset in the limited-view case of 60°, the proposed method demonstrates significant enhancements in peak signal-to-noise ratio and structural similarity index by 23.08% and 7.14%, respectively, concurrently reducing mean squared error by 108.91% compared to the traditional method. The results indicate that the proposed method achieves superior reconstruction quality in extremely limited-view cases, when compared to other methods.

Keywords: photoacoustic tomography, multiple diffusion models, limited view, multi-scale priors, sinogram domain