Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet

Photoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI.