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BioMed Research International
Volume 2014, Article ID 485067, 6 pages
http://dx.doi.org/10.1155/2014/485067
Research Article

Investigating the Feasibility of Rapid MRI for Image-Guided Motion Management in Lung Cancer Radiotherapy

1University of Texas, Southwestern Medical Center, Dallas, TX 75235, USA
2University of Sydney, Sydney, NSW 2006, Australia
3Stanford University, Stanford, CA 95305, USA
4University of Utah, Salt Lake City, UT 84112, USA

Received 17 April 2013; Revised 6 November 2013; Accepted 7 November 2013; Published 12 January 2014

Academic Editor: Jack Yang

Copyright © 2014 Amit Sawant et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. J. Keall, G. S. Mageras, J. M. Balter et al., “The management of respiratory motion in radiation oncology report of AAPM Task Group 76,” Medical Physics, vol. 33, no. 10, pp. 3874–3900, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. G. S. Mageras, A. Pevsner, E. D. Yorke et al., “Measurement of lung tumor motion using respiration-correlated CT,” International Journal of Radiation Oncology Biology Physics, vol. 60, no. 3, pp. 933–941, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Keall, “4-Dimensional Computed Tomography Imaging and Treatment Planning,” Seminars in Radiation Oncology, vol. 14, no. 1, pp. 81–90, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. N. M. Wink, C. Panknin, and T. D. Solberg, “Phase versus amplitude sorting of 4D-CT data,” Journal of Applied Clinical Medical Physics, vol. 7, no. 1, pp. 77–85, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Cai, P. W. Read, and K. Sheng, “The effect of respiratory motion variability and tumor size on the accuracy of average intensity projection from four-dimensional computed tomography: an investigation based on dynamic MRI,” Medical Physics, vol. 35, no. 11, pp. 4974–4981, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Yamamoto, U. Langner, B. W. Loo Jr., J. Shen, and P. J. Keall, “Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients,” International Journal of Radiation Oncology Biology Physics, vol. 72, no. 4, pp. 1250–1258, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. G. F. Persson, D. E. Nygaard, C. Brink et al., “Deviations in delineated GTV caused by artefacts in 4DCT,” Radiotherapy and Oncology, vol. 96, no. 1, pp. 61–66, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Mori, S. Ko, T. Ishii, and K. Nishizawa, “Effective doses in four-dimensional computed tomography for lung radiotherapy planning,” Medical Dosimetry, vol. 34, no. 1, pp. 87–90, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Plathow, H. Hof, S. Kuhn et al., “Therapy monitoring using dynamic MRI: analysis of lung motion and intrathoracic tumor mobility before and after radiotherapy,” European Radiology, vol. 16, no. 9, pp. 1942–1950, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Plathow, S. Ley, C. Fink et al., “Analysis of intrathoracic tumor mobility during whole breathing cycle by dynamic MRI,” International Journal of Radiation Oncology Biology Physics, vol. 59, no. 4, pp. 952–959, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Von Siebenthal, G. Székely, U. Gamper, P. Boesiger, A. Lomax, and P. Cattin, “4D MR imaging of respiratory organ motion and its variability,” Physics in Medicine and Biology, vol. 52, no. 6, article 1547, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Biederer, J. Dinkel, G. Remmert et al., “4D-imaging of the lung: reproducibility of lesion size and displacement on helical CT, MRI, and cone beam CT in a ventilated ex vivo system,” International Journal of Radiation Oncology Biology Physics, vol. 73, no. 3, pp. 919–926, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Cai, Z. Chang, Z. Wang, W. Paul Segars, and F.-F. Yin, “Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study,” Medical Physics, vol. 38, no. 12, pp. 6384–6394, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. A. C. S. Brau, P. J. Beatty, S. Skare, and R. Bammer, “Comparison of reconstruction accuracy and efficiency among autocalibrating data-driven parallel imaging methods,” Magnetic Resonance in Medicine, vol. 59, no. 2, pp. 382–395, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Foskey, A. Gash, Q. Han et al., “A software toolkit for multi-image registration and segmentation in IGRT and ART,” Medical Physics, vol. 34, article 2351, 2007. View at Publisher · View at Google Scholar
  16. K. Wijesooriya, E. Weiss, V. Dill et al., “Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm,” Medical Physics, vol. 35, no. 4, pp. 1251–1260, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Kashani, M. Hub, J. M. Balter et al., “Objective assessment of deformable image registration in radiotherapy: a multi-institution study,” Medical Physics, vol. 35, no. 12, pp. 5944–5953, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Plathow, M. Schoebinger, C. Fink et al., “Quantification of lung tumor volume and rotation at 3D dynamic parallel MR imaging with view sharing: preliminary results,” Radiology, vol. 240, no. 2, pp. 537–545, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Fink, M. Puderbach, J. Biederer et al., “Lung MRI at 1.5 and 3 tesla: observer preference study and lesion contrast using five different pulse sequences,” Investigative Radiology, vol. 42, no. 6, pp. 377–383, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. U. I. Attenberger, M. Ingrisch, O. Dietrich et al., “Time-resolved 3D pulmonary perfusion MRI: comparison of different k-space acquisition strategies at 1.5 and 3 T,” Investigative radiology, vol. 44, no. 9, pp. 525–531, 2009. View at Google Scholar · View at Scopus
  21. A. J. E. Raaijmakers, B. W. Raaymakers, and J. J. W. Lagendijk, “Integrating a MRI scanner with a 6 MV radiotherapy accelerator: dose increase at tissue-air interfaces in a lateral magnetic field due to returning electrons,” Physics in Medicine and Biology, vol. 50, no. 7, pp. 1363–1376, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. G. Fallone, M. Carlone, B. Murray, S. Rathee, and S. Steciw, “Investigations in the design of a novel Linac-MRI system,” International Journal of Radiation Oncology Biology Physics, vol. 69, no. 3, p. S19, 2007. View at Publisher · View at Google Scholar
  23. D. E. Constantin, R. Fahrig, and P. J. Keall, “A study of the effect of in-line and perpendicular magnetic fields on beam characteristics of electron guns in medical linear accelerators,” Medical Physics, vol. 38, no. 7, pp. 4174–4185, 2011. View at Publisher · View at Google Scholar · View at Scopus