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Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal

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dc.contributor.author Guan, Steven
dc.contributor.author Khan, Amir A.
dc.contributor.author Sikdar, Siddartha
dc.contributor.author Chitnis, Parag V.
dc.date.accessioned 2019-07-02T17:36:34Z
dc.date.available 2019-07-02T17:36:34Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/1920/11546
dc.description.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.
dc.language.iso en_US en_US
dc.publisher Journal of Biomedical and Health Informatics en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.title Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal en_US
dc.type Article en_US
dc.identifier.doi 10.1109/JBHI.2019.2912935


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