Affiliations: [a] School of Communication and Information Engineering, Shanghai University, Shanghai, China
| [b] Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| [c] School of Software and Electrical Engineering, Swinburne University of Technology, John Street, Hawthorn, VIC, Australia
Correspondence:
[*]
Corresponding authors: Xin Gao, Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology (CAS), No. 88 Keling Rd, Suzhou 215163, Jiangsu Province, China. Tel.: +86 512 69588089; Fax: +86 512 69588088; E-mail: xingaosam@yahoo.com and Cishen Zhang, School of Software and Electrical Engineering, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, Australia. Tel.: +61 3 9214 8496; Fax: +61 3 9214 8264; E-mail: cishenzhang@swin.edu.au.
Note: [1] Yechen Zhu and Yangchuan Liu are primary authors and contributed equally to this work.
Abstract: BACKGROUND:Due to large dimensional matrix multiplications, the existing iterative algorithms for cone beam computed tomography (CBCT) reconstruction often face problems of heavy computational workload and large volume of memory usage. OBJECTIVE:This study proposes and tests an iterative algorithm of 3DA-TVAL3 for fast reconstruction of CBCT images using undersampled measurement data and the reduced amount of computer memories. METHODS:In order to reduce computational workload and computer memories based on the sparsity of the CBCT measurement matrix, the proposed iterative algorithm applies elementwise scalar multiplications in the iterative computation to search for optimal solution. Through a number of tests on three different CT data sets with different number of projections, the reconstruction performance of the proposed algorithm is compared with that of two accelerated iterative algorithms and the conventional FDK algorithm. RESULTS:The visual and quantitative evaluations using the normalized mean square error, peak signal to noise ratio and structural similarity metrics demonstrated the faster computational time and the higher image quality of using the proposed 3DA-TVAL3 algorithm than using other conventional algorithms under comparison. CONCLUSIONS:The proposed 3DA-TVAL3 algorithm can perform efficient and fast computation of CBCT reconstruction using the reduced amount of computer memories.
