Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Prediction and Classification of Respiratory Motion

Part of the book series: Studies in Computational Intelligence ((SCI,volume 525))

  • 875 Accesses

Abstract

Rapid developments in radiotherapy systems open a new era for the treatment of thoracic and abdominal tumors with accurate dosimetry [1].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. P.J. Keall, G.S. Mageras, J.M. Balter, R.S. Emery, K.M. Forster, S.B. Jiang, J.M. Kapatoes, D.A. Low, M.J. Murphy, B.R. Murray, C.R. Ramsey, M.B. Van Herk, S.S. Vedam, J.W. Wong, E. Yorke, The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med. Phys. 33(10), 3874–3900 (2006)

    Article  Google Scholar 

  2. R.I. Berbeco, S. Nishioka, H. Shirato, G.T.Y. Chen, S.B. Jiang, Residual motion of lung tumours in gated radiotherapy with external respiratory surrogates. Phys. Med. Biol. 50(16), 3655–3667 (2005)

    Article  Google Scholar 

  3. Y.D. Mutaf, C.J. Scicutella, D. Michalski, K. Fallon, E.D. Brandner, G. Bednarz, M.S. Huq, A simulation study of irregular respiratory motion and its dosimetric impact on lung tumors. Phys. Med. Biol. 56(3), 845–859 (2011)

    Article  Google Scholar 

  4. J.R. Wong, L. Grimm, M. Uematsu, R. Oren, C.W. Cheng, S. Merrick, P. Schiff, Image-guided radiotherapy for prostate cancer by CT-linear accelerator combination: prostate movements and dosimetric considerations. Int. J. Radiat. Oncol. Biol. Phys. 61(2), 561–569 (2005)

    Article  Google Scholar 

  5. M.J. Fitzpatrick, G. Starkschall, J.A. Antolak, J. Fu, H. Shukla, P.J. Keall, P. Klahr, R. Mohan, Displacement-based binning of time-dependent computed tomography image data sets. Med. Phys. 33(1), 235–246 (2006)

    Article  Google Scholar 

  6. J. Ehrhardt, R. Werner, D. Säring, T. Frenzel, W. Lu, D. Low, H. Handels, An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. Med. Phys. 34(2), 711–721 (2007)

    Article  Google Scholar 

  7. K.M. Langen, D.T.L. Jones, Organ motion and its management. Int. J. Radiat. Oncol. Biol. Phys. 50(1), 265–278 (2001)

    Article  Google Scholar 

  8. R. Lu, R.J. Radke, L. Hong, C. Chui, J. Xiong, E. Yorke, A. Jackson, Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy. IEEE Trans. Biomed. Eng. 53(5), 908–920 (2006)

    Article  Google Scholar 

  9. Y. Li, J. Lei, A feasible solution to the beam-angle-optimization problem in radiotherapy planning with a DNA-based genetic algorithm. IEEE Trans. Biomed. Eng. 57(3), 499–508 (2010)

    Article  MathSciNet  Google Scholar 

  10. E. Chin, K. Otto, Investigation of a novel algorithm for true 4D-VMAT planning with comparison to tracked, gated and static delivery. Med. Phys. 38(5), 2698–2707 (2011)

    Article  Google Scholar 

  11. A.A. Patel, J.A. Wolfgang, A. Niemierko, T.S. Hong, T. Yock, N.C. Choi, Implications of respiratory motion as measured by four-dimensional computed tomography for radiation treatment planning of esophageal cancer. Int. J. Radiat. Oncol. Biol. Phys. 74(1), 290–296 (2009)

    Article  Google Scholar 

  12. Q.J. Wu, D. Thongphiew, Z. Wang, V. Chankong, F.F. Yin, The impact of respiratory motion and treatment technique on stereotactic body radiation therapy for liver cancer. Med. Phys. 5(4), 1440–1451 (2008)

    Article  Google Scholar 

  13. T. Depuydt, D. Verellen, O. Haas, T. Gevaert, N. Linthout, M. Duchateau, K. Tournel, T. Reynders, K. Leysen, M. Hoogeman, G. Storme, M. De Ridder, Geometric accuracy of a novel gimbals based radiation therapy tumor tracking system. Radiother. Oncol. 98(3), 365–372 (2011)

    Article  Google Scholar 

  14. Q. Ren, S. Nishioka, H. Shirato, R.I. Berbeco, Adaptive prediction of respiratory motion for motion compensation radiotherapy. Phys. Med. Biol. 52(22), 6651–6661 (2007)

    Article  Google Scholar 

  15. H. Shirato, S. Shimizu, T. Kunieda, K. Kitamura, M. van Herk, K. Kagei, T. Nishioka, S. Hashimoto, K. Fujita, H. Aoyama, K. Tsuchiya, K. Kudo, K. Miyasaka, Physical aspects of a real-time tumor-tracking system for gated radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 48(4), 1187–1195 (2000)

    Article  Google Scholar 

  16. M.J. Murphy, Tracking moving organs in real time. Semin. Radiat. Oncol. 14(1), 91–100 (2004)

    Article  Google Scholar 

  17. M. Isaksson, J. Jalden, M.J. Murphy, On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications. Med. Phys. 32(12), 3801–3809 (2005)

    Article  Google Scholar 

  18. S.S. Vedam, P.J. Keall, A. Docef, D.A. Todor, V.R. Kini, R. Mohan, Predicting respiratory motion for four-dimensional radiotherapy. Med. Phys. 31(8), 2274–2283 (2004)

    Article  Google Scholar 

  19. C. Shi, N. Papanikolaou, Tracking versus gating in the treatment of moving targets. Eur. Oncol. Dis. 1, 83–86 (2007)

    Google Scholar 

  20. D. Putra, O.C.L. Haas, J.A. Mills, K.J. Burnham, A multiple model approach to respiratory motion prediction for real-time IGRT. Phys. Med. Biol. 53(6), 1651–1663 (2008)

    Article  Google Scholar 

  21. D. Ruan, Kernel density estimation-based real-time prediction for respiratory motion. Phys. Med. Biol. 55(5), 1311–1326 (2010)

    Article  Google Scholar 

  22. S. J. Lee, Y. Motai and M. Murphy, Respiratory motion estimation with hybrid implementation of extended Kalman filter. IEEE Trans. Ind. Electron. 59(11), 4421–4432 (2012)

    Google Scholar 

  23. A. Kalet, G. Sandison, H. Wu, R. Schmitz, A state-based probabilistic model for tumor respiratory motion prediction. Phys. Med. Biol. 55(24), 7615–7631 (2010)

    Article  Google Scholar 

  24. D. Ruan, P. Keall, Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning. Phys. Med. Biol. 55(11), 3011–3025 (2010)

    Article  Google Scholar 

  25. N. Riaz, P. Shanker, R. Wiersma, O. Gudmundsson, W. Mao, B. Widrow, L. Xing, Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression. Phys. Med. Biol. 54(19), 5735–5748 (2009)

    Article  Google Scholar 

  26. R. Wernera, J. Ehrhardt, R. Schmidt, H. Handels, Patient-specific finite element modeling of respiratory lung motion using 4D CT image data. Med. Phys. 36(5), 1500–1510 (2009)

    Article  Google Scholar 

  27. M.J. Murphy, D. Pokhrel, Optimization of an adaptive neural network to predict breathing. Med. Phys. 36(1), 40–47 (2009)

    Article  Google Scholar 

  28. M.J. Murphy, S. Dieterich, Comparative performance of linear and nonlinear neural networks to predict irregular breathing. Phys. Med. Biol. 51(22), 5903–5914 (2006)

    Article  Google Scholar 

  29. P.S. Verma, H. Wu, M.P. Langer, I.J. Das, G. Sandison, Survey: real-time tumor motion prediction for image guided radiation treatment. Comput. Sci. Eng. 13(5), 24–35 (2011)

    Google Scholar 

  30. I. Buzurovic, K. Huang, Y. Yu, T.K. Podder, A robotic approach to 4D real-time tumor tracking for radiotherapy. Phys. Med. Biol. 56(5), 1299–1318 (2011)

    Article  Google Scholar 

  31. D. Putra, O.C.L. Haas, J.A. Mills, K.J. Bumham, Prediction of tumour motion using interacting multiple model filter. Int. Conf. Adv. Med. Signal Inf. Process. 1–4 (2006)

    Google Scholar 

  32. X. Tang, G.C. Sharp, S.B. Jiang, Fluoroscopic tracking of multiple implanted fiducial markers using multiple object tracking. Phys. Med. Biol. 52(14), 4081–4098 (2007)

    Article  Google Scholar 

  33. H.D. Kubo, P. Len, S. Minohara, H. Mostafavi, Breathing synchronized radiotherapy program at the University of California Davis Cancer Center. Med. Phys. 27(2), 346–353 (2000)

    Article  Google Scholar 

  34. A. Schweikard, G. Glosser, M. Bodduluri, M.J. Murphy, J.R. Adler, Robotic motion compensation for respiratory movement during radiosurgery. Comput. Aided Surg. 5(4), 263–277 (2000)

    Article  Google Scholar 

  35. L.I. Cervino, Y. Jiang, A. Sandhu, S.B. Jiang, Tumor motion prediction with the diaphragm as a surrogate: a feasibility study. Phys. Med. Biol. 55(9), 221–229 (2010)

    Article  Google Scholar 

  36. S.-M. Hong, B.-H. Jung, D. Ruan, Real-time prediction of respiratory motion based on a local dynamic model in an augmented space. Phys. Med. Biol. 56(6), 1775–1789 (2011)

    Article  Google Scholar 

  37. K. Malinowski, T.J. McAvoy, R. George, S. Dietrich, W.D.D’Souza, Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions. Int. J. Radiat. Oncol. Biol. Phys. 82(5), 1665–1673 (2011)

    Google Scholar 

  38. K. Bush, I.M. Gagne, S. Zavgorodni, W. Ansbacher, W. Beckham, Dosimetric validation of acuros XB with monte carlo methods for photon dose calculations. Med. Phys. 38(4), 2208–2221 (2011)

    Article  Google Scholar 

  39. L.I. Cervino, J. Du, S.B. Jiang, MRI-guided tumor tracking in lung cancer radiotherapy. Phy. Med. Biol. 56(13), 3773–3785 (2011)

    Article  Google Scholar 

  40. E.W. Pepina, H. Wu, H. Shirato, Dynamic gating window for compensation of baseline shift in respiratory-gated radiation therapy. Med. Phys. 38(4), 1912–1918 (2011)

    Article  Google Scholar 

  41. P.R. Poulsenb, B. Cho, A. Sawant, D. Ruan, P.J. Keall, Detailed analysis of latencies in image-based dynamic MLC tracking. Med. Phys. 37(9), 4998–5005 (2010)

    Article  Google Scholar 

  42. T. Roland, P. Mavroidis, C. Shi, N. Papanikolaou, Incorporating system latency associated with real-time target tracking radiotherapy in the dose prediction step. Phys. Med. Biol. 55(9), 2651–2668 (2010)

    Article  Google Scholar 

  43. D. Yang, W. Lu, D.A. Low, J.O. Deasy, A.J. Hope, I. El Naqa, 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling. Med. Phys. 35(10), 4577–4590 (2008)

    Article  Google Scholar 

  44. M. Schwarz, J.V.D. Geer, M.V. Herk, J.V. Lebesque, B.J. Mijnheer, E.M.F. Damen, Impact of geometrical uncertainties on 3D CRT and IMRT dose distributions for lung cancer treatment. Int. J. Radiat. Oncol. Biol. Phys. 65(4), 1260–1269 (2006)

    Article  Google Scholar 

  45. M. Kakar, H. Nystr¨om, L.R. Aarup, T.J. Nøttrup, D.R. Olsen, Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). Phys. Med. Biol. 50(19), 4721–4728 (2005)

    Article  Google Scholar 

  46. J. Wilbert, J. Meyer, K. Baier, M. Guckenberger, C. Herrmann, R. Hess, C. Janka, L. Ma, T. Mersebach, A. Richter, M. Roth, K. Schilling, M. Flentje, Tumor tracking and motion compensation with an adaptive tumor tracking system (ATTS) System description and prototype testing. Med. Phys. 35(9), 3911–3921 (2008)

    Article  Google Scholar 

  47. I. Buzurovic, T.K. Podder, K. Huang, Y. Yu, Tumor motion prediction and tracking in adaptive radiotherapy. IEEE Int. Conf. Bioinform. Bioeng. 273–278 (2010)

    Google Scholar 

  48. R. Zeng, J.A. Fessler, J.M. Balter, Estimating 3-D respiratory motion from orbiting views by tomographic image registration. IEEE Trans. Med. Imag. 26(2), 153–163 (2007)

    Article  Google Scholar 

  49. W. Bai, S.M. Brady, Motion correction and attenuation correction for respiratory gated PET images. IEEE Trans. Med. Imag. 30(2), 351–365 (2011)

    Article  Google Scholar 

  50. D. Sarrut, B. Delhay, P. Villard, V. Boldea, M. Beuve, P. Clarysse, A comparison framework for breathing motion estimation methods from 4-D imaging. IEEE Trans. Med. Imag. 26(12), 1636–1648 (2007)

    Google Scholar 

  51. J. Ehrhardt, R. Werner, A. Schmidt-Richberg, H. Handels, statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE Trans. Med. Imag. 30(2), 251–265 (2011)

    Article  Google Scholar 

  52. A.P. King, K.S. Rhode, R.S. Razavi, T.R. Schaeffter, An adaptive and predictive respiratory motion model for image-guided interventions: theory and first clinical application. IEEE Trans. Med. Imag. 28(12), 2020–2032 (2009)

    Article  Google Scholar 

  53. N.A. Ablitt, J. Gao, J. Keegan, L. Stegger, D.N. Firmin, G.-Z. Yang, Predictive cardiac motion modeling and correction with partial least squares regression. IEEE Trans. Med. Imag. 23(10), 1315–1324 (2004)

    Article  Google Scholar 

  54. K. Nakagawa, K. Yoda, Y. Masutani, K. Sasaki, K. Ohtomo, A rod matrix compensator for small-field intensity modulated radiation therapy: a preliminary phantom study. IEEE Trans. Biomed. Eng. 54(5), 943–946 (2007)

    Article  Google Scholar 

  55. V. Agostini, M. Knaflitz, F. Molinari, Motion artifact reduction in breast dynamic infrared imaging. IEEE Trans. Biomed. Eng. 56(3), 903–906 (2009)

    Article  Google Scholar 

  56. H. Tadayyon, A. Lasso, A. Kaushal, P. Guion, G. Fichtinger, Target motion tracking in MRI-guided transrectal robotic prostate biopsy. IEEE Trans. Biomed. Eng. 58(11), 3135–3142 (2011)

    Article  Google Scholar 

  57. J. He, G.J. O’Keefe, S.J. Gong, G. Jones, T. Saunder, A.M. Scott, M. Geso, A novel method for respiratory motion gated with geometric Sensitivity of the scanner in 3D PET. IEEE Trans. Nucl. Sci. 55(5), 2557–2565 (2008)

    Article  Google Scholar 

  58. A.S. Naini, T.-Y. Lee, R.V. Patel, A. Samani, Estimation of lung’s air volume and its variations throughout respiratory CT image sequences. IEEE Trans. Biomed. Eng. 58(1), 152–158 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Texas A&M University—Texarkana, Texarkana, TX, USA

    Suk Jin Lee

  2. Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

    Yuichi Motai

Authors
  1. Suk Jin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuichi Motai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suk Jin Lee .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Lee, S.J., Motai, Y. (2014). Introduction. In: Prediction and Classification of Respiratory Motion. Studies in Computational Intelligence, vol 525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41509-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41509-8_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41508-1

  • Online ISBN: 978-3-642-41509-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation