Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object_Journal of Biomedical Engineering

Authors：

CAI Jiangze ¹ , DUAN Xiaoman ^1,2 , QI Hongliang ² , CHEN Yusi ² , MA Jianhui ¹ ,  ZHOU Linghong ¹ ,  XU Yuan ¹

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P.R.China;
2. Guangzhou Huaduan Technology Limited Company, Guangzhou 510700, P.R.China;

Corresponding author：

ZHOU Linghong, Email: smart@smu.edu.cn; XU Yuan, Email: yuanxu@smu.edu.cn

Keywords：

cone-beam computed tomography; geometric calibration; iterative reconstruction; free trajectory

DOI：

10.7507/1001-5515.202101066

Video：

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Abstract Full text Figures/Tables Video References Cited by

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.

Citation： CAI Jiangze, DUAN Xiaoman, QI Hongliang, CHEN Yusi, MA Jianhui, ZHOU Linghong, XU Yuan. Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object. Journal of Biomedical Engineering, 2021, 38(5): 951-959. doi: 10.7507/1001-5515.202101066 Copy

1.	Ouadah S, Stayman J W, Gang G J, et al. Self-calibration of cone-beam CT geometry using 3D–2D image registration. Phys Med Biol, 2016, 61(7): 2613-2632.
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4.	Jaffray D A, Siewerdsen J H, Wong J W, et al. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys, 2002, 53(5): 1337-1349.
5.	Jacobson M W, Ketcha M D, Capostagno S, et al. A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories. Phys Med Biol, 2018, 63(2): 25030.
6.	Patel V, Chityala R N, Hoffmann K R, et al. Self-calibration of a cone-beam micro-CT system. Med Phys, 2009, 36(1): 48-58.
7.	Noo F, Clack R, White T A, et al. The dual-ellipse cross vertex path for exact reconstruction of long objects in cone-beam tomography. Phys Med Biol, 1998, 43(4): 797-810.
8.	Katsevich A. Image reconstruction for the circle and line trajectory. Phys Med Biol, 2004, 49(22): 5059-5072.
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10.	Yu Z, Lauritsch G, Dennerlein F, et al. Extended ellipse-line-ellipse trajectory for long-object cone-beam imaging with a mounted C-arm system. Phys Med Biol, 2016, 61(4): 1829-1851.
11.	Herbst M, Schebesch F, Berger M, et al. Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging. Med Phys, 2015, 42(5): 2718-2729.
12.	Zeng G L, Gullberg G T. A cone-beam tomography algorithm for orthogonal circle-and-line orbit. Phys Med Biol, 1992, 37(3): 563-577.
13.	Tang X, Ning R. A cone beam filtered backprojection (CB-FBP) reconstruction algorithm for a circle-plus-two-arc orbit. Med Phys, 2001, 28(6): 1042-1055.
14.	Ouadah S, Jacobson M, Stayman J W, et al. Task-driven orbit design and implementation on a robotic C-arm system for cone-beam CT. Proc SPIE Int Soc Opt Eng, 2017, 10132: 101320H.
15.	Boyd S P, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 2010, 3(1): 1-122.
16.	Pan W, Sootla A, Stan G B. Distributed reconstruction of nonlinear networks: An ADMM approach. IFAC Proceedings Volumes, 2014, 47(3): 3208-3213.
17.	Matamoros J, Fosson S M, Magli E, et al. Distributed ADMM for in-network reconstruction of sparse signals with innovations. IEEE T Signal Inf Pr, 2015, 1(4): 225-234.
18.	Jia Xun, Lou Yifei, Lewis J, et al. GPU-based fast low dose cone beam CT reconstruction via total variation. J Xray Sci Technol, 2011, 19(2): 139-154.
19.	Nien H, Fessler J A. Fast X-Ray CT image reconstruction using a linearized augmented lagrangian method with ordered subsets. IEEE Trans Med Imaging, 2015, 34(2): 388-399.
20.	Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process, 2004, 13(4): 600-612.
21.	Rougee A, Picard C L, Trousset Y L, et al. Geometrical calibration for 3D X-ray imaging. Proc SPIE, 1993, 1987: 161-169.
22.	Kyriakou Y, Lapp R M, Hillebrand L, et al. Simultaneous misalignment correction for approximate circular cone-beam computed tomography. Phys Med Biol, 2008, 53(22): 6267-6289.
23.	Wicklein J, Kunze H, Kalender W A, et al. Image features for misalignment correction in medical flat-detector CT. Med Phys, 2012, 39(8): 4918-4931.
24.	Yao Lisha, Dong Zeng, Chen Gaofeng, et al. Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model. Phys Med Biol, 2019, 64(3): 35018.

1. Ouadah S, Stayman J W, Gang G J, et al. Self-calibration of cone-beam CT geometry using 3D–2D image registration. Phys Med Biol, 2016, 61(7): 2613-2632.
2. Pauwels R. Cone beam CT for dental and maxillofacial imaging: dose matters: Table 1. Radiat Prot Dosim, 2015, 165(1-4): 156-161.
3. Boone J M, Nelson T R, Lindfors K K, et al. Dedicated breast CT: Radiation dose and image quality evaluation. Radiology, 2001, 221(3): 657-667.
4. Jaffray D A, Siewerdsen J H, Wong J W, et al. Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys, 2002, 53(5): 1337-1349.
5. Jacobson M W, Ketcha M D, Capostagno S, et al. A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories. Phys Med Biol, 2018, 63(2): 25030.
6. Patel V, Chityala R N, Hoffmann K R, et al. Self-calibration of a cone-beam micro-CT system. Med Phys, 2009, 36(1): 48-58.
7. Noo F, Clack R, White T A, et al. The dual-ellipse cross vertex path for exact reconstruction of long objects in cone-beam tomography. Phys Med Biol, 1998, 43(4): 797-810.
8. Katsevich A. Image reconstruction for the circle and line trajectory. Phys Med Biol, 2004, 49(22): 5059-5072.
9. Katsevich A. Image reconstruction for the circle-and-arc trajectory. Phys Med Biol, 2005, 50(10): 2249-2265.
10. Yu Z, Lauritsch G, Dennerlein F, et al. Extended ellipse-line-ellipse trajectory for long-object cone-beam imaging with a mounted C-arm system. Phys Med Biol, 2016, 61(4): 1829-1851.
11. Herbst M, Schebesch F, Berger M, et al. Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging. Med Phys, 2015, 42(5): 2718-2729.
12. Zeng G L, Gullberg G T. A cone-beam tomography algorithm for orthogonal circle-and-line orbit. Phys Med Biol, 1992, 37(3): 563-577.
13. Tang X, Ning R. A cone beam filtered backprojection (CB-FBP) reconstruction algorithm for a circle-plus-two-arc orbit. Med Phys, 2001, 28(6): 1042-1055.
14. Ouadah S, Jacobson M, Stayman J W, et al. Task-driven orbit design and implementation on a robotic C-arm system for cone-beam CT. Proc SPIE Int Soc Opt Eng, 2017, 10132: 101320H.
15. Boyd S P, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn, 2010, 3(1): 1-122.
16. Pan W, Sootla A, Stan G B. Distributed reconstruction of nonlinear networks: An ADMM approach. IFAC Proceedings Volumes, 2014, 47(3): 3208-3213.
17. Matamoros J, Fosson S M, Magli E, et al. Distributed ADMM for in-network reconstruction of sparse signals with innovations. IEEE T Signal Inf Pr, 2015, 1(4): 225-234.
18. Jia Xun, Lou Yifei, Lewis J, et al. GPU-based fast low dose cone beam CT reconstruction via total variation. J Xray Sci Technol, 2011, 19(2): 139-154.
19. Nien H, Fessler J A. Fast X-Ray CT image reconstruction using a linearized augmented lagrangian method with ordered subsets. IEEE Trans Med Imaging, 2015, 34(2): 388-399.
20. Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process, 2004, 13(4): 600-612.
21. Rougee A, Picard C L, Trousset Y L, et al. Geometrical calibration for 3D X-ray imaging. Proc SPIE, 1993, 1987: 161-169.
22. Kyriakou Y, Lapp R M, Hillebrand L, et al. Simultaneous misalignment correction for approximate circular cone-beam computed tomography. Phys Med Biol, 2008, 53(22): 6267-6289.
23. Wicklein J, Kunze H, Kalender W A, et al. Image features for misalignment correction in medical flat-detector CT. Med Phys, 2012, 39(8): 4918-4931.
24. Yao Lisha, Dong Zeng, Chen Gaofeng, et al. Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model. Phys Med Biol, 2019, 64(3): 35018.

Journal of Biomedical Engineering

Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object

Abstract Full text Figures/Tables Video References Cited by

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