Zusammenfassung
In computed tomography, image reconstruction from an insufficient angular range of projection data is called limited angle tomography. Due to missing data, reconstructed images suffer from artifacts, which cause boundary distortion, edge blurring, and intensity biases. Recently, deep learning methods have been applied very successfully to this problem in simulation studies.
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Huang Y, Würfl T, Breininger K, et al. Some investigations on robustness of deep learning in limited angle tomography. Proc MICCAI. 2018; p. 145–153.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Huang, Y., Würfl, T., Breininger, K., Liu, L., Lauritsch, G., Maier, A. (2019). Abstract: Some Investigations on Robustness of Deep Learning in Limited Angle Tomography. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_6
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DOI: https://doi.org/10.1007/978-3-658-25326-4_6
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