A Novel Reconstruction of the Sparse-View CBCT Algorithm for Correcting Artifacts and Reducing Noise

Author:

Zhang Jie1ORCID,He Bing1,Yang Zhengwei1,Kang Weijie1

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

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference27 articles.

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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

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