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19 March 2014 An experimental study on the noise correlation properties of CBCT projection data
Hua Zhang, Luo Ouyang, Jianhua Ma, Jing Huang, Wufan Chen, Jing Wang
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Proceedings Volume 9033, Medical Imaging 2014: Physics of Medical Imaging; 90332R (2014) https://doi.org/10.1117/12.2043151
Event: SPIE Medical Imaging, 2014, San Diego, California, United States
Abstract
In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on-board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction of low-dose CBCT. The analyses of the repeated measurements show that noise correlation coefficients are non-zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first- and second- order neighbors are 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation results in a lower noise level as compared to the PWLS criterion without considering the noise correlation at the matched resolution.
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Hua Zhang, Luo Ouyang, Jianhua Ma, Jing Huang, Wufan Chen, and Jing Wang "An experimental study on the noise correlation properties of CBCT projection data", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90332R (19 March 2014); https://doi.org/10.1117/12.2043151
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KEYWORDS
Sensors
Data acquisition
Data modeling
Data processing
Imaging systems
Reconstruction algorithms
X-rays
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