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In photoacoustic tomography (PAT), the acoustic pressure waves produced byoptical excitation are measured by an array of detectors and used toreconstruct an image. Sparse spatial sampling and limited-view detection aretwo common challenges faced in PAT. Reconstructing from incomplete data usingstandard methods results in severe streaking artifacts and blurring. We proposea modified convolutional neural network (CNN) architecture termed DenseDilation UNet (DD-UNet) for correcting artifacts in 3D PAT. The DD-Netleverages the benefits of dense connectivity and dilated convolutions toimprove CNN performance. We compare the proposed CNN in terms of image qualityas measured by the multiscale structural similarity index metric to the FullyDense UNet (FD-UNet). Results demonstrate that the DD-Net consistentlyoutperforms the FD-UNet and is able to more reliably reconstruct smaller imagefeatures.

Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction