Tracking and Prediction of Tumor Movement in the Abdomen

Margrit Betke, Jason Ruel, Gregory C. Sharp, Steve B. Jiang, David P. Gierga, and George T. Y. Chen

Abstract

Methods for tracking and prediction of abdominal tumor movement under free breathing conditions are proposed. Tumor position is estimated by tracking surgically implanted clips surrounding the tumor. The clips are segmented from fluoroscopy videos taken during pre-radiotherapy simulation sessions. After the clips have been tracked during an initial observation phase, motion models are computed and used to predict tumor position in subsequent frames. Two methods are proposed and compared that use Fourier analysis to evaluate the quasi-periodic tumor movements due to breathing. Results indicate that the methods have the potential to estimate mobile tumor position to within a couple of millimeters for precise delivery of radiation.

References

  1. Ashkenazy, Y, Ivanov, PC, Peng, SH CK, Goldberger, AL, and Stanley, HE (2001). Magnitude and sign correlations in heartbeat fluctuations. Phys Rev Lett, 86(9):1900-1903.
  2. Balter, JM, Lam, KL, McGinn, CJ, Lawrence, TS, and Haken, RKT (1998). Improvement of CT-based treatment planning models of abdominal targets using static exhalation imaging. Int J Radiat Oncol Biol Phys, 41(4):939-943.
  3. Bettermann, H, Cysarz, D, and Leeuwen, PV (2002). Comparison of two different approaches in the detection of intermittent cardioresperatory coordination during night sleep. BMC Physiology, 4(2):1-18.
  4. Cancer Facts and Figures 2005, American Cancer Society. http://www.cancer.org.
  5. Chen, QS, Weinhous, MS, Deibel, FC, Ciezki, JP, and Macklis, RM (2001). Fluoroscopic study of tumor motion due to breathing: facilitating precise radiation therapy for lung cancer patients. Med Phys, 28(9):1850-1856.
  6. Collins, RT, Gross, R, and Shi, J (2002). Silhouette-based human identification from body shape and gait. In Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pages 366-371, Washington, DC.
  7. Cutler, R and Davis, L S (2000). Robust periodic motion and motion symmetry detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2615-2622, Hilton Head Island, SC.
  8. Fagiani, C, Betke, M, and Gips, J (2002). Evaluation of tracking methods for humancomputer interaction. In Proceedings of the IEEE Workshop on Applications in Computer Vision, pages 121-126, Orlando, FL.
  9. Forsyth, DA and Ponce, J (2003). Computer Vision, A Modern Approach, pp. 373-398. Prentice Hall, NJ.
  10. Ge, X and Smyth, P (2000). Deformable Markov model templates for time-series pattern matching. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 81-90, Boston, MA.
  11. Gierga, DP, Chen, GTY, Kung, JH, Betke, M, Lombardi, J, Willett, CG (2004). Quantification of respiration-induced abdominal tumor motion and its impact on IMRT dose distributions. Int J Radiat Oncol Biol Phys, 58(5):1584-1595.
  12. Gierga, DP, Brewer, J, Sharp, GC, Betke, M, Willett, CG, Chen, GTY (2005). The correlation between internal and external markers for abdominal tumors: implications for respiratory gating. Int J Radiat Oncol Biol Phys, 61(5):1551-1558.
  13. Indyk, P, Koudas, N, and Muthukrishnan, S (2000). Identifying representative trends in massive time series data sets using sketches. In Proceedings of the 26th International Conference on Very Large Databases (VLDB'00), pages 363-372, Cairo, Egypt.
  14. Keall, PJ, Kini, VR, Vedam, SS, and Mohan, R (2001). Motion adaptative x-ray therapy: a feasibility study. Phys Med Biol, 46(1):1-10.
  15. Lee, L and Grimson, WEL (2002). Gait analysis for recognition and classification. In Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pages 155-162, Washington, DC.
  16. Little, JJ and Boyd, JE (1998). Recognizing people by their gait. Videre, 1(2):1-32.
  17. Lotric, MB and Stefanovska, A (2000). Syncronization and modulation in the human cardiorespiratory system. Physica A, 286:451-461.
  18. Neicu, T, Shirato, H, Seppenwoolde, Y, and Jiang, SB (2003). Synchronized Moving Aperture Radiation Therapy (SMART): Average tumor trajectory for lung patients. Phys Med Biol, 48(5):587-598.
  19. Noone, G and Howard, S (1995). Investigation of periodic time series using neural networks with adaptive error thresholds. In Proceedings of the International Conference on Neural Networks (ICNN), pages 1541-1545, Western Australia.
  20. Oates, T, Firoiu, L, and Cohen, PR (1999). Clustering time series with hidden Markov models and dynamic time warping. In IJCAI'99 Workshop on Neural, Symbolic, and Reinforcement Methods for Sequence Learning, 5 pages, Stockholm, Sweden.
  21. Rosenzweig, KE, Hanley, J, Mah, D, Mageras, G, Hunt, M, Toner, S, Burman, C, Ling, CC, Mychalczak, B, Fuks, Z, and Leibel, SA (2000). The deep inspiration breath-hold technique in the treatment of inoperable non-small-cell lung cancer. Int J Radiat Oncol Biol Phys, 48(1):81-87.
  22. Schweikard, A, Glosser, G, Bodduluri, M, Murphy, MJ, and Adler, JR (2000). Robotic motion compensation for respiratory movement during radiosurgery. Comput Aided Surg, 5(4):263-277.
  23. Shapiro, L G and Stockman, G C (2001). Computer Vision, page 299. Prentice Hall, NJ.
  24. Shirato, H, Shimizu, S, Kitamura, K, Nishioka, T, Kagei, K, Hashimoto, S, Aoyama, H, Kunieda, T, Shinohara, N, Dosaka-Akita, H, and Miyasaka, K (2000). Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. Int J Radiat Oncol Biol Phys, 48(2):435-442.
  25. Shirato, H, Shimizu, S, Kunieda, T, Kitamura, K, van Herk, M, Kagei, K, Nishioka, T, Hashimoto, S, Fujita, K, Aoyama, H, Tsuchiya, K, Kudo, K, and Miyasaka, K (2000). Physical aspects of a real-time tumor-tracking system for gated radiotherapy. Int J Radiat Oncol Biol Phys, 48(4):1187-1195.
  26. Siebert, W (1986). Circuits, Signals, and Systems. MIT Press.
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Paper Citation


in Harvard Style

Betke M., Ruel J., C. Sharp G., B. Jiang S., P. Gierga D. and George T. Y. Chen A. (2006). Tracking and Prediction of Tumor Movement in the Abdomen . In 6th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2006) ISBN 978-972-8865-55-9, pages 27-37. DOI: 10.5220/0002471800270037


in Bibtex Style

@conference{pris06,
author={Margrit Betke and Jason Ruel and Gregory C. Sharp and Steve B. Jiang and David P. Gierga and and George T. Y. Chen},
title={Tracking and Prediction of Tumor Movement in the Abdomen},
booktitle={6th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2006)},
year={2006},
pages={27-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002471800270037},
isbn={978-972-8865-55-9},
}


in EndNote Style

TY - CONF
JO - 6th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2006)
TI - Tracking and Prediction of Tumor Movement in the Abdomen
SN - 978-972-8865-55-9
AU - Betke M.
AU - Ruel J.
AU - C. Sharp G.
AU - B. Jiang S.
AU - P. Gierga D.
AU - George T. Y. Chen A.
PY - 2006
SP - 27
EP - 37
DO - 10.5220/0002471800270037