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First study of on-treatment volumetric imaging during respiratory gated VMAT
1. S. Erridge, Y. Seppenwoolde, S. Muller, M. van Herk, K. De Jaeger, J. Belderbos, L. Boersma, and J. Lebesque, “Portal imaging to assess set-up errors, tumor motion and tumor shrinkage during conformal radiotherapy of non-small cell lung cancer,” Radiother. Oncol. 66, 75–85 (2003).
2. I. Suramo, M. Päivänsalo, and V. Myllylä, “Cranio-caudal movements of the liver, pancreas and kidneys in respiration,” Acta Radiol. Diagn. (Stockh.) 25, 129–131 (1984).
3. R. Li, E. Mok, D. Chang, M. Daly, B. Loo, M. Diehn, Q. Le, A. Koong, and L. Xing, “Intrafraction verification of gated rapidarc by using beam-level kilovoltage x-ray images,” Int. J. Radiat. Oncol., Biol., Phys. 83, e709–e715 (2012).
4. R. Li, E. Mok, B. Han, A. Koong, and L. Xing, “Evaluation of the geometric accuracy of surrogate-based gated vmat using intrafraction kilovoltage x-ray images,” Med. Phys. 39, 2686–2693 (2012).
5. 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, 660–663 (2008).
7. S. Leng, J. Tang, J. Zambelli, B. Nett, R. Tolakanahalli, G. H. Chen, G. T. Herman, and R. Davidi, “High temporal resolution and streak-free four-dimensional cone-beam computed tomography,” Phys. Med. Biol. 53, 5653–5673 (2008).
9. 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, 5113–5125 (2010).
10. X. Jia, Y. Lou, R. Li, W. Song, and S. Jiang, “GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation,” Med. Phys. 37, 1757–1760 (2010).
12. K. Choi, B. Fahimian, T. Li, T.-S. Suh, and L. Xing, “Enhancement of four-dimensional cone-beam computed tomography by compressed sensing with Bregman iteration,” J. X-Ray Sci. Technol. (in press).
13. Y. Nesterov, “A method for unconstrained convex minimization problem with the rate of convergence O(1/k2),” Dokl. Akad. Nauk. USSR 269, 543–547 (1983).
14. Y. Nesterov, “Gradient methods for minimizing composite objective function,” Technical Report, Center for Operations Research and Econometrics (CORE), Universite Catholique de Louvain, 2007.
15. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci. 2, 183–202 (2009).
16. A. Nemirovski and D. Yudin, Problem Complexity and Method Efficiency in Optimization (Wiley-Interscience, New York, NY, 1983).
18. H. Lee, L. Xing, R. Davidi, R. Li, J. Qian, and R. Lee, “Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints,” Phys. Med. Biol. 57, 2287–2307 (2012).
20. L. Lee, W. Mao, and L. Xing, “The use of EPID-measured leaf sequence files for IMRT dose reconstruction in adaptive radiation therapy,” Med. Phys. 35, 5019–5029 (2008).
21. J. Qian, L. Lee, W. Liu, K. Chu, E. Mok, G. Luxton, Q. Le, and L. Xing, “Dose reconstruction for volumetric modulated arc therapy (VMAT) using cone-beam CT and dynamic log files,” Phys. Med. Biol. 55, 3597–3610 (2010).
22. K. Nakagawa, A. Haga, K. Shiraishi, H. Yamashita, H. Igaki, A. Terahara, K. Ohtomo, S. Saegusa, T. Shiraki, and T. Oritate, “First clinical cone-beam CT imaging during volumetric modulated arc therapy,” Radiother. Oncol. 90, 422–423 (2009).
23. C. Ling, P. Zhang, T. Etmektzoglou, J. Star-lack, M. Sun, E. Shapiro, and M. Hunt, “Acquisition of MV-scatter-free kilovoltage CBCT images during RapidArc or VMAT,” Radiother. Oncol. 100, 145–149 (2011).
24. M. van Herk, L. Ploeger, and J. Sonke, “A novel method for megavoltage scatter correction in cone-beam CT acquired concurrent with rotational irradiation,” Radiother. Oncol. 100, 365–369 (2011).
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To obtain on-treatment volumetric patient anatomy during respiratory gated volumetric modulated arc therapy (VMAT).
On-board imaging device integrated with Linacs offers a viable tool for obtaining patient anatomy during radiation treatment delivery. In this study, the authors acquired beam-level kV images during gated VMAT treatments using a Varian TrueBeam™STx Linac. These kV projection images are triggered by a respiratory gating signal and can be acquired immediately before treatment MV beam on at every breathing cycle during delivery. Because the kV images are acquired with an on-board imaging device during a rotational arc therapy, they provide the patient anatomical information from many different angles or projection views (typically 20–40). To reconstruct the volumetric image representing patient anatomy during the VMAT treatment, the authors used a compressed sensing method with a fast first-order optimization algorithm. The conventional FDK reconstruction was also used for comparison purposes. The method was tested on a dynamic anthropomorphic physical phantom as well as a lung patient.
The reconstructed volumetric images for a dynamic anthropomorphic physical phantom and a lung patient showed clearly visible soft-tissue target as well as other anatomical structures, with the proposed compressed sensing-based image reconstruction method. Compared with FDK, the compressed sensing method leads to a ∼two and threefold increase in contrast-to-noise ratio around the target area in the phantom and patient case, respectively.
The proposed technique provides on-treatment volumetric patient anatomy, with only a fraction (<10%) of the imaging dose used in conventional CBCT procedures. This anatomical information may be valuable for geometric verification and treatment guidance, and useful for verification of treatment dose delivery, accumulation, and adaptation in the future.
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