References
- Alvarez RE, Macovski A. Energy-selective reconstructions in X-ray computerised tomography. Phys Med Biol. 1976;21:733–744.
- Gleason SS, Sari-Sarraf H, Paulus MJ, et al. Reconstruction of multi-energy X-ray computed tomography images of laboratory mice. IEEE Trans Nucl Sci. 1999;46:1081–1086.
- Joseph PM, Spital RD. A method for correcting bone induced artifacts in computed tomography scanners. J Comput Assist Tomogr. 1978;2:100–108.
- Kyriakou Y, Meyer E, Prell D, et al. Empirical beam hardening correction (EBHC) for CT. Med Phys. 2010;37:5179–5187.
- Meagher JM, Mote CD, Skinner HB. CT image correction for beam hardening using simulated projection data. IEEE Trans Nucl Sci. 1990;37:1520–1524.
- Sukovic P, Clinthorne NH. Penalized weighted least-squares image reconstruction for dual energy X-ray transmission tomography. IEEE Trans Med Imaging. 2000;19:1075–1081.
- van Gompel G, van Slambrouck K, Defrise M, et al. Iterative correction of beam hardening artifacts in CT. Med Phys. 2011;38:S36.
- Vedula V, Munshi P. An improved algorithm for beam-hardening corrections in experimental X-ray tomography. NDT E Int. 2008;41:25–31.
- Gjesteby L, De Man B, Jin Y, et al. Metal artifact reduction in CT: where are we after four decades? IEEE Access. 2016;4:5826–5849.
- Meyer E, Raupach R, Lell M, et al. Normalized metal artifact reduction (NMAR) in computed tomography. Med Phys. 2010;37(10):5482–5493.
- Uneri A, Zhang X, Yi T, et al. Known-component metal artifact reduction (KC-MAR) for cone-beam CT. Phys Med Biol. 2019 aug;64(16):165021..
- Jeon S, Lee C-O. A CT metal artifact reduction algorithm based on sinogram surgery. J Xray Sci Technol. 2018;26:413–434.
- Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys. 1988;79:12–49.
- Chan T, Vese L. Active contours without edges. IEEE Trans Image Process. 2001;10:266–277.
- Chan T, Zhu W. Level set based shape prior segmentation. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on San Diego, CA, Vol. 2, IEEE; 2005. p. 1164–1170.
- Chen S, Radke RJ. Level set segmentation with both shape and intensity priors. In: 2009 IEEE 12th International Conference on Computer Vision; Kyoto, Japan, 2009. p. 763–770.
- Cremers D, Sochen N, Schnörr C. Towards recognition-based variational segmentation using shape priors and dynamic labeling. In: Griffin LD, Lillholm M. editors. Scale space methods in computer vision. Berlin: Springer; 2003. p. 388–400. ISBN 978-3-540-44935-5.
- Perona P, Shiota T, Malik J. Anisotropic diffusion. In: ter Haar Romeny BM, editor. Geometry-driven diffusion in computer vision. Computational imaging and vision; Vol. 1. Dordrecht: Springer; 1994. p. 73–92.
- Osher S, Rudin LI. Feature-oriented image enhancement using shock filters. SIAM J Numer Anal. 1990;27:919–940.
- Jaberipour M, Khorram E, Karimi B. Particle swarm algorithm for solving systems of nonlinear equations. Comput Math Appl. 2011;62:566–576.
- Euler L. Formulae generales pro translatione quacunque corporum rigidorum. Novi Commentarii academiae scientiarum Petropolitanae. 1776;20:189–207.
- Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: MHS’95, Proceedings of the Sixth International Symposium on Micro Machine and Human Science; Nagoya, Japan, 1995. p. 39–43.
- Hubbell JH, Seltzer SM. Tables of X-ray mass attenuation coefficients and mass energy- absorption coefficients (version 1.4) [Online]. Gaithersburg: National Institute of Standards and Technology; 2004. (Originally published as NISTIR), p. 5632. Available from: http://physics.nist.gov/xaamdi