Introduction

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

Abstract

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

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    K.M. Langen, D.T.L. Jones, Organ motion and its management. Int. J. Radiat. Oncol. Biol. Phys. 50(1), 265–278 (2001)CrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)MathSciNetCrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    M.J. Murphy, Tracking moving organs in real time. Semin. Radiat. Oncol. 14(1), 91–100 (2004)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 19.
    C. Shi, N. Papanikolaou, Tracking versus gating in the treatment of moving targets. Eur. Oncol. Dis. 1, 83–86 (2007)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 21.
    D. Ruan, Kernel density estimation-based real-time prediction for respiratory motion. Phys. Med. Biol. 55(5), 1311–1326 (2010)CrossRefGoogle Scholar
  22. 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. 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)CrossRefGoogle Scholar
  24. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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)CrossRefGoogle Scholar
  27. 27.
    M.J. Murphy, D. Pokhrel, Optimization of an adaptive neural network to predict breathing. Med. Phys. 36(1), 40–47 (2009)CrossRefGoogle Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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. 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)CrossRefGoogle Scholar
  31. 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. 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)CrossRefGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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)CrossRefGoogle Scholar
  37. 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. 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)CrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 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)CrossRefGoogle Scholar
  41. 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)CrossRefGoogle Scholar
  42. 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)CrossRefGoogle Scholar
  43. 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)CrossRefGoogle Scholar
  44. 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)CrossRefGoogle Scholar
  45. 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)CrossRefGoogle Scholar
  46. 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)CrossRefGoogle Scholar
  47. 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. 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)CrossRefGoogle Scholar
  49. 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)CrossRefGoogle Scholar
  50. 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. 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)CrossRefGoogle Scholar
  52. 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)CrossRefGoogle Scholar
  53. 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)CrossRefGoogle Scholar
  54. 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)CrossRefGoogle Scholar
  55. 55.
    V. Agostini, M. Knaflitz, F. Molinari, Motion artifact reduction in breast dynamic infrared imaging. IEEE Trans. Biomed. Eng. 56(3), 903–906 (2009)CrossRefGoogle Scholar
  56. 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)CrossRefGoogle Scholar
  57. 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)CrossRefGoogle Scholar
  58. 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceTexas A&M University—TexarkanaTexarkanaUSA
  2. 2.Department of Electrical and Computer EngineeringVirginia Commonwealth UniversityRichmondUSA

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