TY - JOUR

T1 - Data correlation based noise level estimation for cone beam projection data

AU - Bai, Ti

AU - Yan, Hao

AU - Ouyang, Luo

AU - Staub, David

AU - Wang, Jing

AU - Jia, Xun

AU - Jiang, Steve B.

AU - Mou, Xuanqin

N1 - Funding Information:
This work was supported in part by National Key Research and Development Program of China (No. 2016YFA0202003), in part by National Natural Science Foundation of China (No. 61571359),
Funding Information:
This work was supported in part by National Key Research and Development Program of China (No. 2016YFA0202003), in part by National Natural Science Foundation of China (No. 61571359), in part by NIH (1R01CA154747-01, 1R21CA178787-01A1 and 1R21EB017978-01A1), in part by China Scholarship Council.

PY - 2017

Y1 - 2017

N2 - BACKGROUND: In regularized iterative reconstruction algorithms, the selection of regularization parameter depends on the noise level of cone beam projection data. OBJECTIVE: Our aim is to propose an algorithm to estimate the noise level of cone beam projection data. METHODS: We first derived the data correlation of cone beam projection data in the Fourier domain, based on which, the signal and the noise were decoupled. Then the noise was extracted and averaged for estimation. An adaptive regularization parameter selection strategy was introduced based on the estimated noise level. Simulation and real data studies were conducted for performance validation. RESULTS: There exists an approximately zero-energy double-wedge area in the 3D Fourier domain of cone beam projection data. As for the noise level estimation results, the averaged relative errors of the proposed algorithm in the analytical/MC/spotlight-mode simulation experiments were 0.8%, 0.14% and 0.24%, respectively, and outperformed the homogeneous area based as well as the transformation based algorithms. Real studies indicated that the estimated noise levels were inversely proportional to the exposure levels, i.e., the slopes in the log-log plot were -1.0197 and -1.049 with respect to the short-scan and half-fan modes. The introduced regularization parameter selection strategy could deliver promising reconstructed image qualities. CONCLUSIONS: Based on the data correlation of cone beam projection data in Fourier domain, the proposed algorithm could estimate the noise level of cone beam projection data accurately and robustly. The estimated noise level could be used to adaptively select the regularization parameter.

AB - BACKGROUND: In regularized iterative reconstruction algorithms, the selection of regularization parameter depends on the noise level of cone beam projection data. OBJECTIVE: Our aim is to propose an algorithm to estimate the noise level of cone beam projection data. METHODS: We first derived the data correlation of cone beam projection data in the Fourier domain, based on which, the signal and the noise were decoupled. Then the noise was extracted and averaged for estimation. An adaptive regularization parameter selection strategy was introduced based on the estimated noise level. Simulation and real data studies were conducted for performance validation. RESULTS: There exists an approximately zero-energy double-wedge area in the 3D Fourier domain of cone beam projection data. As for the noise level estimation results, the averaged relative errors of the proposed algorithm in the analytical/MC/spotlight-mode simulation experiments were 0.8%, 0.14% and 0.24%, respectively, and outperformed the homogeneous area based as well as the transformation based algorithms. Real studies indicated that the estimated noise levels were inversely proportional to the exposure levels, i.e., the slopes in the log-log plot were -1.0197 and -1.049 with respect to the short-scan and half-fan modes. The introduced regularization parameter selection strategy could deliver promising reconstructed image qualities. CONCLUSIONS: Based on the data correlation of cone beam projection data in Fourier domain, the proposed algorithm could estimate the noise level of cone beam projection data accurately and robustly. The estimated noise level could be used to adaptively select the regularization parameter.

KW - Fourier domain

KW - cone beam projections

KW - noise level estimation

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U2 - 10.3233/XST-17266

DO - 10.3233/XST-17266

M3 - Article

C2 - 28697578

AN - SCOPUS:85036459299

VL - 25

SP - 907

EP - 926

JO - Journal of X-Ray Science and Technology

JF - Journal of X-Ray Science and Technology

SN - 0895-3996

IS - 6

ER -