Abstract
Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and in vivo datasets demonstrates that our method achieves better reconstructions with improved speed.