Sparse angle CT reconstruction based on neural radial field

Dongjie Zhao; Hai Nan; Kai Zhao

doi:10.1117/12.3036735

13 July 2024 Sparse angle CT reconstruction based on neural radial field

Dongjie Zhao, Hai Nan, Kai Zhao

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132080T (2024) https://doi.org/10.1117/12.3036735
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

To address the issues of extensive artifacts and noise in sparse-angle computed tomography (CT) reconstruction, we propose a neural radial field-based sparse-angle CT reconstruction algorithm. Specifically, we utilize both newly synthesized projection views and original projection views as inputs to increase the projection views, thereby enhancing the quality of the reconstruction results. However, the newly synthesized projection images exhibit a certain degree of blur. To address this issue, we incorporate a deblurring module into the network to optimize sampling during the process, thereby achieving deblurring effects. This approach overcomes the challenges of sparse-angle CT reconstruction. We demonstrate CT reconstruction with fewer projection data (20 projections) and validate the effectiveness of our algorithm on a publicly available clinical dataset. Experimental results show that the proposed algorithm can improve the quality of CT image reconstruction, providing a feasible and effective method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongjie Zhao, Hai Nan, and Kai Zhao "Sparse angle CT reconstruction based on neural radial field", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132080T (13 July 2024); https://doi.org/10.1117/12.3036735

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