Keywords:
DECT, spectrum estimation, iterative recon-struction
Knowing the X-ray spectrum is important to dual energy computed tomography (DECT) reconstruction. Whereas the spectrum is not always available in practice. In addition, the reconstructed image of DECT is extremely sensitive to noise which demands special noise suppression strategy in recon-struction algorithm design. Hence the iterative reconstruction methods by inducing regularization to elevate the image quality attract more attention. In this paper, we develop an iterative algorithm with simultaneously updating the spectrum and the image of DECT, which did not need the knowledge of the spectrum in advance. Namely, spectrum estimation and iterative reconstruction are incorporated in an iterative framework. The estimated spectrum and the reconstructed images are obtained si-multaneously with the proposed algorithm. Two sets of simulation experiment with different phantoms were adopted for evaluation. In the experiment, the spectrum estimated by the image based reconstruction results are utilized as the initially estimated spectrum. In comparison with the initially estimated spectrum, experimental results validated that the proposed method can increase the accuracy of the estimated spectrum. Besides, the quality of the reconstructed density images has been improved by this work at the same time.
- Shaojie Chang
- Institute of Image processing and Pattern recognition, Xi'an Jiaotong University, Xi’an, Shaanxi 710049, China.
- Xuanqin Mou
- Institute of Image processing and Pattern recognition, Xi'an Jiaotong University, Xi’an, Shaanxi 710049, China.
- C. E. Cann, “Quantitative ct for determination of bone mineral density: a review,” Radiology; (United States), vol. 166:2, no. 2, pp. 509–522, 1988.
- P. B. Dunscombe, D. E. Katz, and A. J. Stacey, “Some practical aspects of dual-energy ct scanning.,” British Journal of Radiology, vol. 57, no. 673, pp. 82–87, 1984.
- G. D. Chiro, R. A. Brooks, R. M. Kessler, G. S. Johnston, A. E. Jones, J. R. Herdt, and W. T. Sheridan, “Tissue signatures with dual-energy computed tomography.,” Ra-diology, vol. 131, no. 2, pp. 521–3, 1979.
- R. E. Alvarez and A. Macovski, “Energy-selective recon-structions in x-ray computerized tomography.,” Physics in Medicine Biology, vol. 21, no. 5, pp. 733–44, 1976.
- T. P. Szczykutowicz and G. H. Chen, “Dual energy ct using slow kvp switching acquisition and prior image constrained compressed sensing.,” Physics in Medicine Biology, vol. 55, no. 21, pp. 6411–29, 2010.
- T. Niu, X. Dong, M. Petrongolo, and L. Zhu, “Iterative image-domain decomposition for dual-energy ct.,” Med-ical Physics, vol. 41, no. 4, p. 041901, 2014.
- Y. Zhao, X. Zhao, and P. Zhang, “An extended algebraic reconstruction technique (e-art) for dual spectral ct,” IEEE Transactions on Medical Imaging, vol. 34, no. 3, pp. 761–768, 2014.
- J. A. Fessler, I. A. Elbakri, P. Sukovic, and N. H. Clinthorne, “Maximum-likelihood dual-energy tomo-graphic image reconstruction,” Proceedings of Spie, vol. 4684, pp. 38–49, 2002.
- Q. Xu, X. Mou, S. Tang, W. Hong, Y. Zhang, and T. Luo, “Implementation of penalized-likelihood statis-tical reconstruction for polychromatic dual-energy ct,” Proceedings of SPIE - The International Society for Medical Imaging, vol. 7258, pp. 72585I–72585I–9, 2009.
- E. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in medicine and biolo-gy, vol. 53, no. 17, p. 4777, 2008.
- Q. Xu, H. Yu, X. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose x-ray ct reconstruction via dictionary learn-ing,” Medical Imaging, IEEE Transactions on, vol. 31, no. 9, pp. 1682–1697, 2012.
- E. Y. Sidky, L. Yu, X. Pan, Y. Zou, and M. Vannier, “A robust method of x-ray source spectrum estimation from transmission measurements: Demonstrated on computer simulated, scatter-free transmission data,” Journal of Ap-plied Physics, vol. 97, no. 12, pp. 124701 – 124701–11, 2005.
- Y. Yang, X. Mou, and X. Chen, “A robust x-ray tube spectra measuring method by attenuation data - art. no. 61423k,” Proceedings of SPIE - The International Society for Optical Engineering, pp. 61423K–61423K–8, 2006.
- W. Zhao, K. Niu, S. Schafer, and K. Royalty, “An indirec-t transmission measurement-based spectrum estimation method for computed tomography,” Physics in Medicine Biology, vol. 60, no. 1, pp. 339–357, 2015.
- W. Zhao, Q. Zhang, and T. Niu, “Segmentation-free x-ray energy spectrum estimation for computed tomography,” 2016.
- I. Elbakri and J. Fessler, “Statistical image reconstruction for polyenergetic x-ray computed tomography,” Medical Imaging, IEEE Transactions on, vol. 21, no. 2, pp. 89–99, 2002.
- R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, “A limited memory algorithm for bound constrained optimization,” Siam Journal on Scientific Computing, vol. 16, no. 5, pp. 1190–1208, 1995.
- S. Chang and X. Mou, “A statistical iterative reconstruc-tion framework for dual energy computed tomography without knowing tube spectrum,” in SPIE Optical Engi-neering + Applications, p. 99671L, 2016.
- J. A. Fessler, “http://web.eecs.umich.edu/ fessler/,”