Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction

Photoacoustic computed tomography (PACT) combines the high optical absorption contrast of optical excitation with the deep tissue penetration enabled by ultrasonic detection, making it a promising imaging modality. However, constraints on transducer density and angular coverage often result in sparse-view acquisitions that cause severe artifacts. In this work, we propose ND-net, a progressive dual-branch adversarial diffusion framework for efficient and high-quality sparse-view PACT reconstruction. The framework uses two stages where a residual artifact-reconstruction branch estimates structured sparse-view artifacts, followed by an adversarially guided full-view diffusion branch that refines structural information. By enabling flexible reverse transitions, ND-net supports large-step diffusion sampling with only four reverse iterations, improving inference efficiency. Experiments on simulated vessel data, circular phantom measurements, and in vivo mouse abdomen imaging demonstrate improved reconstruction quality over representative analytical and learning-based methods under highly sparse acquisition conditions. These results indicate that ND-net improves sparse-view PACT reconstruction while enabling efficient inference.