Correction of the projection center of rotation based on the sinogram using translation matching method.

Qi Zhao; Yuqing Zhao; Changhong Cong; Dongjiang JI; Lili Qin; Xiaodong Chen; Chunhong HU

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Correction of the projection center of rotation based on the sinogram using translation matching method.

Author: Qi ZHAO ¹ ; Yuqing ZHAO ¹ ; Changhong CONG ² ; Dongjiang JI ³ ; Lili QIN ¹ ; Xiaodong CHEN ⁴ ; Chunhong HU ⁵
Author Information

1. College of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, P.R.China.
2. The Dental Hospital of Tianjin Medical University, Tianjin 300070, P.R.China.
3. The School of Science, Tianjin University of Technology and Education, Tianjin 300222, P.R.China.
4. Key Laboratory of Opto-electronic Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, P.R.China.
5. College of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, P.R.China.chunhong_hu@hotmail.com.
Publication Type:Journal Article
Keywords: L1-norm; OTSU; center of rotation; computed tomography reconstruction; sinogram
From: Journal of Biomedical Engineering 2018;35(4):598-605
CountryChina
Language:Chinese
Abstract: The accurate position of the center of rotation (COR) is a key factor to ensure the quality of computed tomography (CT) reconstructed images. The classic cross-correlation matching algorithm can not satisfy the requirements of high-quality CT imaging when the projection angle is 0 and 180°, and thus needs to be improved and innovated. In this study, considering the symmetric characteristic of the 0° and flipped 180° projection data in sinogram, a novel COR correction algorithm based on the translation and match of the 0° and 180° projection data was proposed. The OTSU method was applied to reduce noise on the background, and the minimum offset of COR was quantified using the -norm, and then a precise COR was obtained for the image correction and reconstruction. The Sheep-Logan simulation model with random gradients and Gaussian noise and the real male SD rats samples which contained the heterogenous tooth image and the homogenous liver image, were adopted to verify the performance of the new algorithm and the cross-correlation matching algorithm. The results show that the proposed algorithm has better robustness and higher accuracy of the correction (when the sampled data is from 10% to 50% of the full projection data, the COR value can still be measured accurately using the proposed algorithm) with less computational burden compared with the cross-correlation matching algorithm, and it is able to significantly improve the quality of the reconstructed images.