Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
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Date
2019
Authors
Guan, Steven
Khan, Amir A.
Sikdar, Siddartha
Chitnis, Parag V.
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Biomedical and Health Informatics
Abstract
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.