Research on cone-beam CT image reconstruction algorithm based on compressed sensing

Teng Wang; Jun Lang

doi:10.1117/12.2643619

9 October 2022 Research on cone-beam CT image reconstruction algorithm based on compressed sensing

Teng Wang, Jun Lang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 1224616 (2022) https://doi.org/10.1117/12.2643619
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

With the development of computed tomography (CT) technology, how to reconstruct high-quality pathological images under low-dose X-ray radiation has become the focus of attention. Compressed sensing theory has become a research hotspot because of its good reconstruction quality under incomplete data. In compressed sensing theory, the design of projection matrix and total variational model is the key factor to determine the quality of reconstruction. In this paper, after the cone beam projection is rearranged into fan beam, a fan beam projection matrix model is designed. Based on the traditional distance driven model, the forward and back projection distance driven model is used to roughly estimate the image, and the multilevel scheme (MLS) is introduced to further improve the reconstruction quality of the image. The experimental results show that under the condition of incomplete projection data, the reconstructed image has less artifacts, complete edge details and clear image.

Citation Download Citation

Teng Wang and Jun Lang "Research on cone-beam CT image reconstruction algorithm based on compressed sensing", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 1224616 (9 October 2022); https://doi.org/10.1117/12.2643619

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