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Title: Deep learning approach to improve tangential resolution in photoacoustic tomography
Authors: Rajendran, Praveenbalaji
Pramanik, Manojit
Keywords: Engineering::Bioengineering
Issue Date: 2020
Source: Rajendran, P., & Pramanik, M. (2020). Deep learning approach to improve tangential resolution in photoacoustic tomography. Biomedical Optics Express, 11(12), 7311-7323. doi:10.1364/BOE.410145
Journal: Biomedical Optics Express
Abstract: In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image.
ISSN: 2156-7085
DOI: 10.1364/BOE.410145
Rights: © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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