  Photoacoustic tomography (PAT) is a newly emerged imaging modality which
enables both high optical contrast and acoustic depth of penetration.
Reconstructing images of photoacoustic tomography from limited amount of senser
data is among one of the major challenges in photoacoustic imaging. Previous
works based on deep learning were trained in supervised fashion, which directly
map the input partially known sensor data to the ground truth reconstructed
from full field of view. Recently, score-based generative models played an
increasingly significant role in generative modeling. Leveraging this
probabilistic model, we proposed Rotation Consistency Constrained Score-based
Generative Model (RCC-SGM), which recovers the PAT images by iterative sampling
between Langevin dynamics and a constraint term utilizing the rotation
consistency between the images and the measurements. Our proposed method can
generalize to different measurement processes (32.29 PSNR with 16 measurements
under random sampling, whereas 28.50 for supervised counterpart), while
supervised methods need to train on specific inverse mappings.
