Abstract
Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes.
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© 2017 Springer-Verlag GmbH Deutschland
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Hammernik, K., Würfl, T., Pock, T., Maier, A. (2017). A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_25
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DOI: https://doi.org/10.1007/978-3-662-54345-0_25
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-54344-3
Online ISBN: 978-3-662-54345-0
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