Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation

نویسندگان

چکیده

Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging, especially when the extremely small. Both analytical and iterative models need more for effective modeling. Deep learning methods have gained prevalence due to their excellent performances, but such success mainly same dataset does not generalize across datasets with different distributions. Hereby we propose ExtraPolationNetwork limited-angle CT via introduction of sinogram extrapolation module, which theoretically justified. The module complements extra information boots model generalizability. Extensive experimental results show that our achieves state-of-the-art performance on NIH-AAPM dataset, similar existing approaches. More importantly, using significantly improves generalization capability unseen (e.g., COVID-19 LIDC datasets) compared

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-87231-1_9