  In photoacoustic tomography (PAT), the acoustic pressure waves produced by
optical excitation are measured by an array of detectors and used to
reconstruct an image. Sparse spatial sampling and limited-view detection are
two common challenges faced in PAT. Reconstructing from incomplete data using
standard methods results in severe streaking artifacts and blurring. We propose
a modified convolutional neural network (CNN) architecture termed Dense
Dilation UNet (DD-UNet) for correcting artifacts in 3D PAT. The DD-Net
leverages the benefits of dense connectivity and dilated convolutions to
improve CNN performance. We compare the proposed CNN in terms of image quality
as measured by the multiscale structural similarity index metric to the Fully
Dense UNet (FD-UNet). Results demonstrate that the DD-Net consistently
outperforms the FD-UNet and is able to more reliably reconstruct smaller image
features.
