Open Access
MATEC Web Conf.
Volume 76, 2016
20th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
Article Number 02003
Number of page(s) 6
Section Systems
Published online 21 October 2016
  1. L. Xing, B. Thorndyke, E. Schreibmann, Y. Yang, T.-F. Li, G.-Y. Kim, G. Luxton and A. Koong, “Overview of Image-Guided Radiation Therapy,” Medical Dosimetry, vol. 31, no. 2, pp. 91–112, 2006. [CrossRef] [Google Scholar]
  2. L. A. Dawson and M. B. Sharpe, “Image-guided radiotherapy: rationale, benefits, and limitations.”, Lancet Oncology, no. 7, pp. 848–858, 2006. [CrossRef] [Google Scholar]
  3. L. A. Dawson and D. A. Jaffray, “Advances in image-guided radiation therapy,” J Cin Oncol, vol. 25, pp. 938–946, 2007. [CrossRef] [Google Scholar]
  4. M. Baumann, T. Holscher and D. Zips, “The future of IGRT - cost benefit analysis,” Acta Oncologica, vol. 47, no. 7, pp. 1188–1192, 2008. [CrossRef] [Google Scholar]
  5. M. Lecchi, P. Fossati, F. Elisei, R. Orecchia and G. Lucignani, “Current concepts on imaging in radiotherapy,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 35, pp. 821–837, 2008. [CrossRef] [Google Scholar]
  6. D. Verellen, M. De Ridder and G. Storme, “A (short) history of image-guided radiotherapy,” Radiotherapy and Oncology, vol. 86, pp. 4–13, 2008. [CrossRef] [Google Scholar]
  7. G. S. Mageras, A. Pevsner and E. D. Yorke, “Measurement of lung tumor motion using respiration-correlated CT,” Int J Radiat Oncol Biol Phys, no. 60, pp. 933–941, 2004. [CrossRef] [Google Scholar]
  8. P. Keall, “4-dimensional computed tomography imaging and treatment planning,” Seminars in Radiation Oncology, vol. 14, pp. 81–90, 2004. [CrossRef] [Google Scholar]
  9. V. Boldea, G. C. Sharp, S. B. Jiang and D. Sarrut, “4D-CT lung motion estimation with deformable registration: Quantification of motion nonlinearity and hysteresis,” Medical Physics, vol. 35, pp. 1008–1018, 2008. [CrossRef] [Google Scholar]
  10. Y. Sun, S. Dieterich, B. Cho and P. J. Keall, “An analysis of thoracic and abdominal tumour motion for stereotactic body radiotherapy patients,” Physics in Medicine and Biology, vol. 53, pp. 3623–3640, 2008. [CrossRef] [Google Scholar]
  11. S. Mori, M. Endo, S. Komatsu, T. Yashiro, S. Kandatsu and M. Baba, “Four-dimensional measurement of lung tumor displacement using 256-multi-slice CT-scanner,” Lung Cancer, vol. 56, pp. 59–67, 2007. [CrossRef] [Google Scholar]
  12. A. Trofimov, E. Rietzel and H. Lu, “Temporo-spatial IMRT optimization: Concepts, implementation and initial results,” Phys Med Biol, no. 50, pp. 2779–2798, 2005. [CrossRef] [Google Scholar]
  13. E. M. Leter, F. Cademartiri, P. C. Levendag, H. Stam and P. J. Nowak, “Four-dimensional multislice computed tomography for determination of respiratory lung tumor motion in conformal radiotherapy,” International Journal of Radiation Oncology, Biology, Physics, vol. 62, no. 3, pp. 888–892, 1 July 2005. [CrossRef] [Google Scholar]
  14. J. Wong, M. B. Sharpe and D. A. Jaffray, “The use of active breathing control (ABC) to reduce margin for breathing motion,” Int J Radiat Oncol Biol Phys, no. 44, pp. 911–919, 1999. [CrossRef] [Google Scholar]
  15. R. Zeng, J. A. Fessler and J. M. Balter, “Respiratory motion estimation from slowly rotating x-ray projections: theory and simulation,” Medical Physics, vol. 32, no. 4, pp. 984–991, April 2005. [CrossRef] [Google Scholar]
  16. F. Khan, G. Bell, J. Antony, M. Palmer, P. Balter, K. Bucci and M. J. Chapman, “The use of 4DCT to reduce lung dose: a dosimetric analysis,” Medical Dosimetry, vol. 34, no. 4, pp. 273–278, 2009. [CrossRef] [Google Scholar]
  17. G. 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, vol. 35, pp. 660–663, 2008. [CrossRef] [Google Scholar]
  18. P. J. Keall, G. Starkschall and H. Shukla, “Acquiring 4D thoracic CT scans using a multislice helical method,” Phys Med Biol, no. 49, pp. 2053–2067, 2004. [CrossRef] [Google Scholar]
  19. E. Yorke, K. E. Rosenzweig, R. Wagman and G. S. Mageras, “Interfractional anatomic variation in patients treated with respiration-gated radiotherapy,” Journal of Applied Clinical Medical Physics, vol. 6, pp. 19–32, 2005. [CrossRef] [Google Scholar]
  20. A. Docef and M. J. Murphy, “Reconstruction of 4D deformed CT for moving anatomy,” International Journal of Computer Assisted Radiology and Surgery, no. 3, pp. 591–598, 2008. [CrossRef] [Google Scholar]
  21. R. S. Brock, A. Docef and M. J. Murphy, “Reconstruction of a cone-beam CT via forward iterative projection matching,” Medical Physics, vol. 37, 2010. [Google Scholar]
  22. D. Staub, A. Docef, R. S. Brock and C. Vaman, “4D Cone-beam CT reconstruction using a motion model based on principal component analysis,” Medical Physics, vol. 38, no. 12, pp. 6212–6220, December 2011. [Google Scholar]
  23. M. J. Murphy and S. Dieterich, “Comparative performance of linear and nonlinear neural networks to predict irregular breathing,” Physics in Medicine and Biology, vol. 51, no. 22, p. 5903, 21 Nov 2006. [CrossRef] [Google Scholar]
  24. M. J. Murphy and D. Pokhrel, “Optimization of an adaptive neural network to predict breathing,” Medical Physics, vol. 36, no. 1, pp. 40–47, January 2009. [CrossRef] [Google Scholar]
  25. A. G. T. Ben-Israel, Generalized Inverses: Theory And Applications, Springer, 2003. [Google Scholar]
  26. E. Zachariah, “4-D Modeling of Displacement Vector Fields for Improved Radiation Therapy,” 2010. [Online]. Available: [Google Scholar]

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