Affiliation:
1. College of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China
Abstract
X-ray tomography is often affected by noise and artifacts during the reconstruction process, such as detector offset, calibration errors, metal artifacts, etc. Conventional algorithms, including FDK and SART, are unable to satisfy the sampling theorem requirements for 3D reconstruction under sparse-view constraints, exacerbating the impact of noise and artifacts. This paper proposes a novel 3D reconstruction algorithm tailored to sparse-view cone-beam computed tomography (CBCT). Drawing upon compressed sensing theory, we incorporate the weighted Schatten p-norm minimization (WSNM) algorithm for 2D image denoising and the adaptive steepest descent projection onto convex sets (ASD-POCS) algorithm, which employs a total variation (TV) regularization term. These inclusions serve to reduce noise and ameliorate artifacts. Our proposed algorithm extends the WSNM approach into three-dimensional space and integrates the ASD-POCS algorithm, enabling 3D reconstruction with digital brain phantoms, clinical medical data, and real projections from our portable CBCT system. The performance of our algorithm surpasses traditional methods when evaluated using root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. Furthermore, our approach demonstrates marked enhancements in artifact reduction and noise suppression.
Funder
Shaanxi Provincial Innovation Capacity Support Plan
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference27 articles.
1. Wang, Y., Yang, T., and Huang, W. (2020, January 20–24). Limited-angle computed tomography reconstruction using combined FDK-based neural network and U-Net. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.
2. Rathore, J.S., Laquai, R., Biguri, A., Soleimani, M., and Vienne, C. (2022, January 8–11). Benchmarking of different reconstruction algorithms for industrial cone-beam CT. Proceedings of the 11th Conference on Industrial Computed Tomography, Wels, Austria (ICT 2022), Wels, Austria.
3. DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography;Zhou;Med. Image Anal.,2022
4. The Shannon sampling theorem—Its various extensions and applications: A tutorial review;Jerri;Proc. IEEE,1977
5. Compressed sensing;Donoho;IEEE Trans. Inf. Theory,2006
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