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Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT
2. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and A. A. Martinez, “Flat-panel cone-beam computed tomography for image-guided radiation therapy,” Int. J. Radiat. Oncol., Biol., Phys. 53(5), 1337–1349 (2002).
3. J. C. Park, S. H. Park, J. H. Kim, S. M. Yoon, S. S. Kim, J. S. Kim, Z. Liu, T. Watkins, and W. Y. Song, “Four-dimensional cone-beam computed tomography and digital tomosynthesis reconstructions using respiratory signals extracted from transcutaneously inserted metal markers for liver SBRT,” Med. Phys. 38(2), 1028–1036 (2011).
4. W. Song, B. Schaly, G. Bauman, J. Battista, and J. Van Dyk, “Image-guided adaptive radiation therapy (IGART): Radiobiological and dose escalation considerations for localized carcinoma of the prostate,” Med. Phys. 32(7), 2193–2203 (2005).
5. J. Hatton, B. McCurdy, and P. B. Greer, “Cone beam computerized tomography: The effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy,” Phys. Med. Biol. 54(15), N329–N346 (2009).
6. S. Yoo and F. F. Yin, “Dosimetric feasibility of cone-beam CT-based treatment planning compared to CT-based treatment planning,” Int. J. Radiat. Oncol., Biol., Phys. 66(5), 1553–1561 (2006).
7. W. Y. Song, S. Kamath, S. Ozawa, S. A. Ani, A. Chvetsov, N. Bhandare, J. R. Palta, C. Liu, and J. G. Li, “A dose comparison study between XVI and OBI CBCT systems,” Med. Phys. 35(2), 480–486 (2008).
9. J. Bian, J. H. Siewerdsen, X. Han, E. Y. Sidky, J. L. Prince, C. A. Pelizzari, and X. Pan, “Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT,” Phys. Med. Biol. 55(22), 6575–6599 (2010).
10. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
11. 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,” Med. Phys. 35(2), 660–663 (2008).
12. K. Choi, J. Wang, L. Zhu, T. S. Suh, S. Boyd, and L. Xing, “Compressed sensing based cone-beam computed tomography reconstruction with a first-order method,” Med. Phys. 37(9), 5113–5125 (2010).
13. J. H. Jørgensen, T. L. Jensen, P. C. Hansen, S. H. Jensen, E. Y. Sidky, and X. Pan, “Accelerated gradient methods for total variation based CT image reconstruction,” the 11th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Potsdam, Germany, 2011).
15. D. L. Donoho, M. Elad, and V. N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
16. L. Ritschl, F. Bergner, C. Fleischmann, and M. Kachelriess, “Improved total variation-based CT image reconstruction applied to clinical data,” Phys. Med. Biol. 56(6), 1545–1561 (2011).
17. E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 4777–4807 (2008).
18. T. P. Szczykutowicz and G. H. Chen, “Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing,” Phys. Med. Biol. 55(21), 6411–6429 (2010).
19. J. Tang, B. E. Nett, and G. H. Chen, “Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms,” Phys. Med. Biol. 54(19), 5781–5804 (2009).
20. J. Wang, T. F. Li, and L. Xing, “Iterative image reconstruction for CBCT using edge-preserving prior,” Med. Phys. 36(1), 252–260 (2009).
22. X. Jia, Y. Lou, J. Lewis, R. Li, X. Gu, C. Men, W. Y. Song, and S. B. Jiang, “GPU-based fast low-dose cone beam CT reconstruction via total variation,” J. X-Ray Sci. Technol. 19(2), 139–154 (2011).
23. X. Jia, Y. Lou, R. Li, W. Y. Song, and S. B. Jiang, “GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation,” Med. Phys. 37(4), 1757–1760 (2010).
24. F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware,” IEEE Trans. Nucl. Sci. 52(3), 654–663 (2005).
27. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
28. D. P. Bertsekas, Nonlinear programming, 2nd ed. (Athena Scientific, Belmont, Mass., 1999).
29. J. C. Ramirez-Giraldo, J. Trzasko, S. Leng, L. Yu, A. Manduca, and C. H. McCollough, “Nonconvex prior image constrained compressed sensing (NCPICCS): Theory and simulations on perfusion CT,” Med. Phys. 38(4), 2157–2167 (2011).
30. J. C. Park, S. H. Park, J. S. Kim, Y. Kim, M. K. Cho, H. K. Kim, Z. Liu, S. B. Jiang, B. Song, and W. Y. Song, “Ultra-fast digital tomosynthesis reconstruction using general-purpose GPU programming for image-guided radiation therapy,” Technol. Cancer Res. Treat. 10(4), 295–306 (2011).
33. J. Wang, H. Guan and T. Solberg, “Inverse determination of the penalty parameter in penalized weighted least-squares algorithm for noise reduction of low-dose CBCT,” Med. Phys. 38(7), 4066–4072 (2011).
34. L. Zhu and L. Xing, “Search for IMRT inverse plans with piecewise constant fluence maps using compressed sensing techniques,” Med. Phys. 36(5), 1895–1905 (2009).
35. J. C. Park, B. Y. Song, S. H. Park, J. S. Kim, H. K. Kim, T. S. Suh, Z. Liu, and W. Y. Song, “Fast, iterative, low-dose, cone-beam computed tomography reconstruction using a gradient projection algorithm (Oral presentation) Joint AAPM/COMP Meeting, Vancouver, Canada (2011).
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