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.
CITATION STYLE
Hammernik, K., Würfl, T., Pock, T., & Maier, A. (2017). A deep learning architecture for limited-angle computed tomography reconstruction. In Informatik aktuell (pp. 92–97). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-662-54345-0_25
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