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      A New Deep Learning Network for Mitigating Limited-view and Under-sampling Artifacts in Ring-shaped Photoacoustic Tomography

      , , , , , ,
      Computerized Medical Imaging and Graphics
      Elsevier BV

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          Abstract

          Photoacoustic tomography (PAT) is a hybrid technique for high-resolution imaging of optical absorption in tissue. Among various transducer arrays proposed for PAT, the ring-shaped transducer array is widely used in cross-sectional imaging applications. However, due to the high fabrication cost, most ring-shaped transducer arrays have a sparse transducer arrangement, which leads to limited-view problems and under-sampling artifacts. To address these issues, we paired conventional PAT reconstruction with deep learning, which recently achieved a breakthrough in image processing and tomographic reconstruction. In this study, we designed a convolutional neural network (CNN) called a ring-array deep learning network (RADL-net), which can eliminate limited-view and under-sampling artifacts in PAT images. The method was validated on a three-quarter ring transducer array using numerical simulation, phantom imaging, and in vivo imaging. Our results indicate that the proposed RADL-net significantly improves the quality of reconstructed images on a three-quarter ring transducer array. The method is also superior to the conventional compressed sensing (CS) algorithm.

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              Deep Learning in Medical Image Analysis

              This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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                Author and article information

                Contributors
                Journal
                Computerized Medical Imaging and Graphics
                Computerized Medical Imaging and Graphics
                Elsevier BV
                08956111
                September 2020
                September 2020
                : 84
                : 101720
                Article
                10.1016/j.compmedimag.2020.101720
                32679469
                2cf2ce65-3d40-4568-b3b7-6fcfc9aead82
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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