D-STAR: Diffusion-Based Sparse Tomographic Angular Recovery for Isotropic-Resolution Photoacoustic Imaging

Anisotropy in imaging systems often results in directional degradation, impairing image quality and complicating subsequent analyses. While multiangle imaging has proven effective in mitigating these effects, it introduces challenges such as extended imaging times and increased excitation doses. To address these limitations in Photoacoustic Tomography (PAT), we propose a novel approach-Diffusion-based Sparse Tomographic Angular Recovery (D-STAR). D-STAR significantly reduces the number of required angles for high-resolution PAT while maintaining image quality comparable to full tomographic angular imaging. By training a diffusion model on a custom 3D PAT dataset, we optimize the balance between spatial and temporal resolutions, signal-to-noise ratio (SNR), and laser exposure. Our experiments with excised brain and vessel phantoms demonstrate that D-STAR produces high-fidelity images suitable for both structural and molecular imaging. This method outperforms existing approaches in static structural recovery and quantitative data extraction, offering substantial improvements in imaging quality, particularly in resolution and contrast. Furthermore, D-STAR enhances flexibility in imaging system design, reducing the need for hardware upgrades while improving temporal resolution and minimizing laser exposure.