Hybrid reconstruction using shearlets and deep learning for sparse x-ray computed tomography

Andi Braimllari; Theodor Cheslerean-Boghiu; Tobias Lasser

doi:10.1117/12.2646385

17 October 2022 Hybrid reconstruction using shearlets and deep learning for sparse x-ray computed tomography

Andi Braimllari, Theodor Cheslerean-Boghiu, Tobias Lasser

Author Affiliations +

Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123040N (2022) https://doi.org/10.1117/12.2646385
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States

Abstract

In sparse X-ray Computed Tomography, the radiation dose to the patient is lowered by measuring fewer projection views compared to a standard protocol. In this work we investigate a hybrid approach combining shearlet representation with deep learning for reconstruction of sparse-view X-ray computed tomography. The proposed method is hybrid in that it reconstructs the parts that can provably be retrieved by utilizing a model-based approach, and it in-paints the parts that provably cannot through a learning-based approach. In doing so, we attempt to benefit from the best aspects of model- and learning-based methods. We demonstrate first promising results on publicly available data.

Citation Download Citation

Andi Braimllari, Theodor Cheslerean-Boghiu, and Tobias Lasser "Hybrid reconstruction using shearlets and deep learning for sparse x-ray computed tomography", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123040N (17 October 2022); https://doi.org/10.1117/12.2646385

ACCESS THE FULL ARTICLE

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY