Work

Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model

Year: 2024

Type: article

Abstract: Photoacoustic tomography, a novel non-invasive imaging modality, combines the principles of optical and acoustic imaging. In scenarios of sparse-view sampling, conventional reconstruction methods significantly degrade image resolution and generate numerous artifacts. To mitigate these constraints, a novel sinogram-domain priors guided extremely sparse-view reconstruction method for photoacoustic tomography boosted by enhanced diffusion model is proposed. The model learns prior information of sinograms under full-ring, 512-projections. In iterative reconstruction, the prior information serves as a constraint in least-squares optimization, facilitating convergence towards more plausible solutions. The method is evaluated using blood vessel simulation, phantoms, and in vivo experimental data. The results show that the proposed method demonstrates superior performance compared to the U-Net method, yielding images of markedly higher quality. Notably, for in vivo data under 32 projections, the sinogram structural similarity improved by ~21% over U-Net. The reconstruction in the sinogram domain for photoacoustic tomography enhances sparse-view imaging capabilities.

Source: Photoacoustics

DOI: 10.1016/j.pacs.2024.100670