Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation
24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
; 12906 LNCS:86-96, 2021.
Article
Dans Anglais
| Scopus | ID: covidwho-1469648
ABSTRACT
Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging, especially when the angle range is extremely small. Both analytical and iterative models need more projections for effective modeling. Deep learning methods have gained prevalence due to their excellent reconstruction performances, but such success is mainly limited within the same dataset and does not generalize across datasets with different distributions. Hereby we propose ExtraPolationNetwork for limited-angle CT reconstruction via the introduction of a sinogram extrapolation module, which is theoretically justified. The module complements extra sinogram information and boots model generalizability. Extensive experimental results show that our reconstruction model achieves state-of-the-art performance on NIH-AAPM dataset, similar to existing approaches. More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e.g., COVID-19 and LIDC datasets) when compared to existing approaches. © 2021, Springer Nature Switzerland AG.
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
langue:
Anglais
Revue:
24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Année:
2021
Type de document:
Article
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