  Photoacoustic imaging is an emerging imaging modality that is based upon the
photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic
pressure waves are measured by an array of detectors and used to reconstruct an
image of the initial pressure distribution. A common challenge faced in PAT is
that the measured acoustic waves can only be sparsely sampled. Reconstructing
sparsely sampled data using standard methods results in severe artifacts that
obscure information within the image. We propose a modified convolutional
neural network (CNN) architecture termed Fully Dense UNet (FD-UNet) for
removing artifacts from 2D PAT images reconstructed from sparse data and
compare the proposed CNN with the standard UNet in terms of reconstructed image
quality.
