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Preface
Limited-angle CT reconstruction with generalized shrinkage operators as regularizers
1. | School of Mathematical Sciences, Capital Normal University, Beijing, 100048, China |
2. | Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, 100048, China |
3. | Division of Ionizing Radiation, National Institute of Metrology, Beijing 100029, China |
Limited-angle reconstruction is a very important but challenging problem in the field of computed tomography (CT) which has been extensively studied for many years. However, some difficulties still remain. Based on the theory of visible and invisible boundary developed by Quinto et.al, we propose a reconstruction model for limited-angle CT, which encodes the visible edges as priors to recover the invisible ones. The new model utilizes generalized shrinkage operators as regularizers to perform edge-preserving smoothing such that the visible edges are employed as anchors to recover piecewise-constant or piecewise-smooth reconstructions, while noises and artifacts are suppressed or removed. This work extends our previous research on limited-angle reconstruction which employs gradient $ \ell_0 $ and $ \ell_1 $ norm regularizers. The effectiveness of the proposed model and its corresponding solving algorithm shall be verified by numerical experiments with simulated data as well as real data.
References:
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A. H. Andersen and A. C. Kak,
Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm, Ultrasonic Imaging, 6 (1984), 81-94.
doi: 10.1177/016173468400600107. |
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D. P. Bertsekas, Incremental gradient, subgradient, and proximal methods for convex optimization: A survey, Optimization for Machine Learning, 2010 (2011), 3. |
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D. P. Bertsekas,
Incremental proximal methods for large scale convex optimization, Mathematical Programming, 129 (2011), 163-195.
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[4] |
S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Now Foundations and Trends® in Machine learning, 2011.
doi: 10.1561/9781601984616. |
[5] |
A. Chambolle and T. Pock,
A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145.
doi: 10.1007/s10851-010-0251-1. |
[6] |
R. Chartrand, Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data, in International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2009, 262–265. |
[7] |
R. Chartrand, Shrinkage mappings and their induced penalty functions, in International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, 1026–1029. |
[8] |
F. Chen, L. Shen and B. W. Suter,
Computing the proximity operator of the $\ell_p$ norm with 0 < p < 1, IET Signal Processing, 10 (2016), 557-565.
|
[9] |
G.-H. Chen, J. Tang and S. Leng,
Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, Medical Physics, 35 (2008), 660-663.
doi: 10.1118/1.2836423. |
[10] |
Z. Chen, X. Jin, L. Li and G. Wang, A limited-angle CT reconstruction method based on anisotropic TV minimization, Physics in Medicine & Biology, 58 (2013), 2119.
doi: 10.1088/0031-9155/58/7/2119. |
[11] |
R. Fahrig, R. Dixon, T. Payne, R. L. Morin, A. Ganguly and N. Strobel,
Dose and image quality for a cone-beam C-arm CT system, Medical Physics, 33 (2006), 4541-4550.
doi: 10.1118/1.2370508. |
[12] |
J. Frikel and E. T. Quinto, Characterization and reduction of artifacts in limited angle tomography, Inverse Problems, 29 (2013), 125007, 21 pp.
doi: 10.1088/0266-5611/29/12/125007. |
[13] |
P. Gilbert,
Iterative methods for the three-dimensional reconstruction of an object from projections, Journal of Theoretical Biology, 36 (1972), 105-117.
doi: 10.1016/0022-5193(72)90180-4. |
[14] |
T. Goldstein and S. Osher,
The split bregman method for L1 regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343.
doi: 10.1137/080725891. |
[15] |
C. Gong, L. Zeng and C. Wang,
Image reconstruction model for limited-angle ct based on prior image induced relative total variation, Applied Mathematical Modelling, 74 (2019), 586-605.
doi: 10.1016/j.apm.2019.05.020. |
[16] |
R. Gordon, R. Bender and G. T. Herman,
Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and x-ray photography, Journal of Theoretical Biology, 29 (1970), 471-481.
doi: 10.1016/0022-5193(70)90109-8. |
[17] |
G. T. Gullberg,
The reconstruction of fan-beam data by filtering the back-projection, Computer Graphics and Image Processing, 10 (1979), 30-47.
doi: 10.1016/0146-664X(79)90033-9. |
[18] |
Q. Huynh-Thu and M. Ghanbari,
Scope of validity of psnr in image/video quality assessment, Electronics Letters, 44 (2008), 800-801.
doi: 10.1049/el:20080522. |
[19] |
F. Jacobs, E. Sundermann, B. De Sutter, M. Christiaens and I. Lemahieu,
A fast algorithm to calculate the exact radiological path through a pixel or voxel space, Journal of Computing and Information Technology, 6 (1998), 89-94.
|
[20] |
M. Jiang and G. Wang,
Convergence of the simultaneous algebraic reconstruction technique (SART), IEEE Transactions on Image Processing, 12 (2003), 957-961.
doi: 10.1109/TIP.2003.815295. |
[21] |
M. Jiang and G. Wang,
Convergence studies on iterative algorithms for image reconstruction, IEEE Transactions on Medical Imaging, 22 (2003), 569-579.
doi: 10.1109/TMI.2003.812253. |
[22] |
J. Kaipio and E. Somersalo,
Statistical inverse problems: discretization, model reduction and inverse crimes, Journal of Computational and Applied Mathematics, 198 (2007), 493-504.
doi: 10.1016/j.cam.2005.09.027. |
[23] |
H. Kudo and T. Saito,
Sinogram recovery with the method of convex projections for limited-data reconstruction in computed tomography, Journal of the Optical Society of America A, 8 (1991), 1148-1160.
|
[24] |
X. Li, Z. Zhu, A. M.-C. So and J. D. Lee, Incremental methods for weakly convex optimization, arXiv: 1907.11687. |
[25] |
A. K. Louis,
Incomplete data problems in X-ray computerized tomography, Numerische Mathematik, 48 (1986), 251-262.
doi: 10.1007/BF01389474. |
[26] |
L. T. Niklason, B. T. Christian, L. E. Niklason, D. B. Kopans, D. E. Castleberry, B. H. Opsahl-Ong, C. E. Landberg, P. J. Slanetz, A. A. Giardino and R. Moore,
Digital tomosynthesis in breast imaging., Radiology, 205 (1997), 399-406.
doi: 10.1148/radiology.205.2.9356620. |
[27] |
E. T. Quinto, Artifacts and visible singularities in limited data X-ray tomography, Sensing and Imaging, 18 (2017), 9.
doi: 10.1007/s11220-017-0158-7. |
[28] |
N. A. B. Riis, J. Frøsig, Y. Dong and P. C. Hansen, Limited-data x-ray CT for underwater pipeline inspection, Inverse Problems, 34 (2018), 034002, 16 pp.
doi: 10.1088/1361-6420/aaa49c. |
[29] |
E. Y. Sidky, C.-M. Kao and X. Pan,
Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT, Journal of X-ray Science and Technology, 14 (2006), 119-139.
|
[30] |
S. Tan, Y. Zhang, G. Wang, X. Mou, G. Cao, Z. Wu and H. Yu, Tensor-based dictionary learning for dynamic tomographic reconstruction, Physics in Medicine & Biology, 60 (2015), 2803.
doi: 10.1088/0031-9155/60/7/2803. |
[31] |
L. H. Thomas, Elliptic problems in linear difference equations over a network, Watson Sci. Comput. Lab. Rept., Columbia University, New York, 1. |
[32] |
C. Wang, L. Zeng, Y. Guo and L. Zhang,
Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data, Inverse Problems & Imaging, 11 (2017), 917-948.
doi: 10.3934/ipi.2017043. |
[33] |
T. Wang, K. Nakamoto, H. Zhang and H. Liu,
Reweighted anisotropic total variation minimization for limited-angle CT reconstruction, IEEE Transactions on Nuclear Science, 64 (2017), 2742-2760.
doi: 10.1109/TNS.2017.2750199. |
[34] |
Z. Wang and A. C. Bovik,
A universal image quality index, IEEE Signal Processing Letters, 9 (2002), 81-84.
|
[35] |
Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli,
Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13 (2004), 600-612.
doi: 10.1109/TIP.2003.819861. |
[36] |
J. Woodworth and R. Chartrand, Compressed sensing recovery via nonconvex shrinkage penalties, Inverse Problems, 32 (2016), 075004, 25 pp.
doi: 10.1088/0266-5611/32/7/075004. |
[37] |
C. Wu and X.-C. Tai,
Augmented lagrangian method, dual methods, and split bregman iteration for ROF, vectorial TV, and high order models, SIAM Journal on Imaging Sciences, 3 (2010), 300-339.
doi: 10.1137/090767558. |
[38] |
D. Wu and L. Zeng,
Limited-angle reverse helical cone-beam CT for pipeline with low rank decomposition, Optics Communications, 328 (2014), 109-115.
doi: 10.1016/j.optcom.2014.04.077. |
[39] |
J. Xu, Y. Zhao, H. Li and P. Zhang, An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography, Inverse Problems, 35 (2019), 085004, 34 pp.
doi: 10.1088/1361-6420/ab08f9. |
[40] |
L. Xu, C. Lu, Y. Xu and J. Jia, Image smoothing via L0 gradient minimization, ACM Transactions on Graphics (TOG), 30 (2011), 174. |
[41] |
X. Xue, S. Zhao, Y. Zhao and P. Zhang, Image reconstruction for limited-angle computed tomography with curvature constraint, Measurement Science and Technology, 30 (2019), 125401. |
[42] |
L. Zhang, L. Zeng and Y. Guo,
$l_0$ regularization based on a prior image incorporated non-local means for limited-angle x-ray ct reconstruction, Journal of X-ray science and technology, 26 (2018), 481-498.
doi: 10.3233/XST-17334. |
[43] |
J. Zhao, Z. Chen, L. Zhang and X. Jin, Unsupervised learnable sinogram inpainting network (SIN) for limited angle CT reconstruction, arXiv: 1811.03911. |
show all references
References:
[1] |
A. H. Andersen and A. C. Kak,
Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm, Ultrasonic Imaging, 6 (1984), 81-94.
doi: 10.1177/016173468400600107. |
[2] |
D. P. Bertsekas, Incremental gradient, subgradient, and proximal methods for convex optimization: A survey, Optimization for Machine Learning, 2010 (2011), 3. |
[3] |
D. P. Bertsekas,
Incremental proximal methods for large scale convex optimization, Mathematical Programming, 129 (2011), 163-195.
doi: 10.1007/s10107-011-0472-0. |
[4] |
S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Now Foundations and Trends® in Machine learning, 2011.
doi: 10.1561/9781601984616. |
[5] |
A. Chambolle and T. Pock,
A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145.
doi: 10.1007/s10851-010-0251-1. |
[6] |
R. Chartrand, Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data, in International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2009, 262–265. |
[7] |
R. Chartrand, Shrinkage mappings and their induced penalty functions, in International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, 1026–1029. |
[8] |
F. Chen, L. Shen and B. W. Suter,
Computing the proximity operator of the $\ell_p$ norm with 0 < p < 1, IET Signal Processing, 10 (2016), 557-565.
|
[9] |
G.-H. Chen, J. Tang and S. Leng,
Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, Medical Physics, 35 (2008), 660-663.
doi: 10.1118/1.2836423. |
[10] |
Z. Chen, X. Jin, L. Li and G. Wang, A limited-angle CT reconstruction method based on anisotropic TV minimization, Physics in Medicine & Biology, 58 (2013), 2119.
doi: 10.1088/0031-9155/58/7/2119. |
[11] |
R. Fahrig, R. Dixon, T. Payne, R. L. Morin, A. Ganguly and N. Strobel,
Dose and image quality for a cone-beam C-arm CT system, Medical Physics, 33 (2006), 4541-4550.
doi: 10.1118/1.2370508. |
[12] |
J. Frikel and E. T. Quinto, Characterization and reduction of artifacts in limited angle tomography, Inverse Problems, 29 (2013), 125007, 21 pp.
doi: 10.1088/0266-5611/29/12/125007. |
[13] |
P. Gilbert,
Iterative methods for the three-dimensional reconstruction of an object from projections, Journal of Theoretical Biology, 36 (1972), 105-117.
doi: 10.1016/0022-5193(72)90180-4. |
[14] |
T. Goldstein and S. Osher,
The split bregman method for L1 regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343.
doi: 10.1137/080725891. |
[15] |
C. Gong, L. Zeng and C. Wang,
Image reconstruction model for limited-angle ct based on prior image induced relative total variation, Applied Mathematical Modelling, 74 (2019), 586-605.
doi: 10.1016/j.apm.2019.05.020. |
[16] |
R. Gordon, R. Bender and G. T. Herman,
Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and x-ray photography, Journal of Theoretical Biology, 29 (1970), 471-481.
doi: 10.1016/0022-5193(70)90109-8. |
[17] |
G. T. Gullberg,
The reconstruction of fan-beam data by filtering the back-projection, Computer Graphics and Image Processing, 10 (1979), 30-47.
doi: 10.1016/0146-664X(79)90033-9. |
[18] |
Q. Huynh-Thu and M. Ghanbari,
Scope of validity of psnr in image/video quality assessment, Electronics Letters, 44 (2008), 800-801.
doi: 10.1049/el:20080522. |
[19] |
F. Jacobs, E. Sundermann, B. De Sutter, M. Christiaens and I. Lemahieu,
A fast algorithm to calculate the exact radiological path through a pixel or voxel space, Journal of Computing and Information Technology, 6 (1998), 89-94.
|
[20] |
M. Jiang and G. Wang,
Convergence of the simultaneous algebraic reconstruction technique (SART), IEEE Transactions on Image Processing, 12 (2003), 957-961.
doi: 10.1109/TIP.2003.815295. |
[21] |
M. Jiang and G. Wang,
Convergence studies on iterative algorithms for image reconstruction, IEEE Transactions on Medical Imaging, 22 (2003), 569-579.
doi: 10.1109/TMI.2003.812253. |
[22] |
J. Kaipio and E. Somersalo,
Statistical inverse problems: discretization, model reduction and inverse crimes, Journal of Computational and Applied Mathematics, 198 (2007), 493-504.
doi: 10.1016/j.cam.2005.09.027. |
[23] |
H. Kudo and T. Saito,
Sinogram recovery with the method of convex projections for limited-data reconstruction in computed tomography, Journal of the Optical Society of America A, 8 (1991), 1148-1160.
|
[24] |
X. Li, Z. Zhu, A. M.-C. So and J. D. Lee, Incremental methods for weakly convex optimization, arXiv: 1907.11687. |
[25] |
A. K. Louis,
Incomplete data problems in X-ray computerized tomography, Numerische Mathematik, 48 (1986), 251-262.
doi: 10.1007/BF01389474. |
[26] |
L. T. Niklason, B. T. Christian, L. E. Niklason, D. B. Kopans, D. E. Castleberry, B. H. Opsahl-Ong, C. E. Landberg, P. J. Slanetz, A. A. Giardino and R. Moore,
Digital tomosynthesis in breast imaging., Radiology, 205 (1997), 399-406.
doi: 10.1148/radiology.205.2.9356620. |
[27] |
E. T. Quinto, Artifacts and visible singularities in limited data X-ray tomography, Sensing and Imaging, 18 (2017), 9.
doi: 10.1007/s11220-017-0158-7. |
[28] |
N. A. B. Riis, J. Frøsig, Y. Dong and P. C. Hansen, Limited-data x-ray CT for underwater pipeline inspection, Inverse Problems, 34 (2018), 034002, 16 pp.
doi: 10.1088/1361-6420/aaa49c. |
[29] |
E. Y. Sidky, C.-M. Kao and X. Pan,
Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT, Journal of X-ray Science and Technology, 14 (2006), 119-139.
|
[30] |
S. Tan, Y. Zhang, G. Wang, X. Mou, G. Cao, Z. Wu and H. Yu, Tensor-based dictionary learning for dynamic tomographic reconstruction, Physics in Medicine & Biology, 60 (2015), 2803.
doi: 10.1088/0031-9155/60/7/2803. |
[31] |
L. H. Thomas, Elliptic problems in linear difference equations over a network, Watson Sci. Comput. Lab. Rept., Columbia University, New York, 1. |
[32] |
C. Wang, L. Zeng, Y. Guo and L. Zhang,
Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data, Inverse Problems & Imaging, 11 (2017), 917-948.
doi: 10.3934/ipi.2017043. |
[33] |
T. Wang, K. Nakamoto, H. Zhang and H. Liu,
Reweighted anisotropic total variation minimization for limited-angle CT reconstruction, IEEE Transactions on Nuclear Science, 64 (2017), 2742-2760.
doi: 10.1109/TNS.2017.2750199. |
[34] |
Z. Wang and A. C. Bovik,
A universal image quality index, IEEE Signal Processing Letters, 9 (2002), 81-84.
|
[35] |
Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli,
Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 13 (2004), 600-612.
doi: 10.1109/TIP.2003.819861. |
[36] |
J. Woodworth and R. Chartrand, Compressed sensing recovery via nonconvex shrinkage penalties, Inverse Problems, 32 (2016), 075004, 25 pp.
doi: 10.1088/0266-5611/32/7/075004. |
[37] |
C. Wu and X.-C. Tai,
Augmented lagrangian method, dual methods, and split bregman iteration for ROF, vectorial TV, and high order models, SIAM Journal on Imaging Sciences, 3 (2010), 300-339.
doi: 10.1137/090767558. |
[38] |
D. Wu and L. Zeng,
Limited-angle reverse helical cone-beam CT for pipeline with low rank decomposition, Optics Communications, 328 (2014), 109-115.
doi: 10.1016/j.optcom.2014.04.077. |
[39] |
J. Xu, Y. Zhao, H. Li and P. Zhang, An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography, Inverse Problems, 35 (2019), 085004, 34 pp.
doi: 10.1088/1361-6420/ab08f9. |
[40] |
L. Xu, C. Lu, Y. Xu and J. Jia, Image smoothing via L0 gradient minimization, ACM Transactions on Graphics (TOG), 30 (2011), 174. |
[41] |
X. Xue, S. Zhao, Y. Zhao and P. Zhang, Image reconstruction for limited-angle computed tomography with curvature constraint, Measurement Science and Technology, 30 (2019), 125401. |
[42] |
L. Zhang, L. Zeng and Y. Guo,
$l_0$ regularization based on a prior image incorporated non-local means for limited-angle x-ray ct reconstruction, Journal of X-ray science and technology, 26 (2018), 481-498.
doi: 10.3233/XST-17334. |
[43] |
J. Zhao, Z. Chen, L. Zhang and X. Jin, Unsupervised learnable sinogram inpainting network (SIN) for limited angle CT reconstruction, arXiv: 1811.03911. |









Parameter | Value |
Distance of X-ray source to rotation center | 600 mm |
Width of detector unit | 0.25 mm |
Number of detector units | 1118 |
Distance of rotation center to detector | 1739.63 mm |
Scanning Angular Interval | 0.5 degree |
Parameter | Value |
Distance of X-ray source to rotation center | 600 mm |
Width of detector unit | 0.25 mm |
Number of detector units | 1118 |
Distance of rotation center to detector | 1739.63 mm |
Scanning Angular Interval | 0.5 degree |
SART+ATV |
SART+ATV |
GAEDS( |
GAEDS( |
SART+ATV |
SART+ATV |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
GAEDS( |
AEDS( |
AEDS( |
GAEDS( |
AEDS( |
AEDS( |
GAEDS( |
SART | AEDS( |
AEDS( |
GAEDS( |
|
PSNR | 16.886 | 51.7035 | 45.4061 | 53.4988 |
SSIM | 0.6822 | 0.9755 | 0.9710 | 0.9808 |
UQI | 0.8155 | 0.9999 | 0.9998 | 0.9999 |
SART | AEDS( |
AEDS( |
GAEDS( |
|
PSNR | 16.886 | 51.7035 | 45.4061 | 53.4988 |
SSIM | 0.6822 | 0.9755 | 0.9710 | 0.9808 |
UQI | 0.8155 | 0.9999 | 0.9998 | 0.9999 |
AEDS( |
AEDS( |
GAEDS( |
|
Rhombus(150°) | |||
Rhombus(140°) | |||
Rhombus(130°) | |||
AEDS( |
AEDS( |
GAEDS( |
|
Rhombus(150°) | |||
Rhombus(140°) | |||
Rhombus(130°) | |||
SART | AEDS( |
AEDS( |
GAEDS( |
||
150 degree | PSNR | 31.7800 | 34.4700 | 35.7580 | 35.5110 |
SSIM | 0.99366 | 0.99496 | 0.99616 | 0.99615 | |
UQI | 0.99030 | 0.99477 | 0.99609 | 0.99590 | |
140 degree | PSNR | 29.4930 | 33.7360 | 33.5110 | 34.1700 |
SSIM | 0.98854 | 0.99426 | 0.99330 | 0.99361 | |
UQI | 0.98333 | 0.99382 | 0.99339 | 0.99433 | |
130 degree | PSNR | 27.2680 | 32.7270 | 32.2330 | 33.7820 |
SSIM | 0.98347 | 0.99272 | 0.98837 | 0.99425 | |
UQI | 0.97162 | 0.99218 | 0.99105 | 0.99388 |
SART | AEDS( |
AEDS( |
GAEDS( |
||
150 degree | PSNR | 31.7800 | 34.4700 | 35.7580 | 35.5110 |
SSIM | 0.99366 | 0.99496 | 0.99616 | 0.99615 | |
UQI | 0.99030 | 0.99477 | 0.99609 | 0.99590 | |
140 degree | PSNR | 29.4930 | 33.7360 | 33.5110 | 34.1700 |
SSIM | 0.98854 | 0.99426 | 0.99330 | 0.99361 | |
UQI | 0.98333 | 0.99382 | 0.99339 | 0.99433 | |
130 degree | PSNR | 27.2680 | 32.7270 | 32.2330 | 33.7820 |
SSIM | 0.98347 | 0.99272 | 0.98837 | 0.99425 | |
UQI | 0.97162 | 0.99218 | 0.99105 | 0.99388 |
Parameter | Value |
Voltage | 140 kV |
Current | 160 mA |
Distance of X-ray source to rotation center | 311.49 mm |
Width of detector unit | 0.127 mm |
Number of detector units | 1920 |
Distance of rotation center to detector | 697.82 mm |
Scanning angular interval | 0.2 degree |
Parameter | Value |
Voltage | 140 kV |
Current | 160 mA |
Distance of X-ray source to rotation center | 311.49 mm |
Width of detector unit | 0.127 mm |
Number of detector units | 1920 |
Distance of rotation center to detector | 697.82 mm |
Scanning angular interval | 0.2 degree |
AEDS( |
AEDS( |
GAEDS( |
AEDS( |
AEDS( |
GAEDS( |
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