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Image-Based Motion Correction

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Machine Learning in Radiation Oncology

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Abstract

This chapter will discuss dedicated machine learning techniques for motion management using imaging information. We will cover a wide range of well-established machine learning techniques, including principal component analysis, linear discriminant analysis, artificial neural networks, and support vector machine, etc. Motion management techniques including both respiratory gating and real-time tumor tracking will be discussed. In this chapter, we will demonstrate how to utilize domain-specific knowledge and prior imaging information to achieve more accurate and robust motion management in radiotherapy. Finally, future research directions in the clinical applications of machine learning for motion management will be discussed.

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References

  1. Berbeco RI, Mostafavi H, Sharp GC, Jiang SB. Towards fluoroscopic respiratory gating for lung tumours without radiopaque markers. Phys Med Biol. 2005;50:4481–90.

    Article  PubMed  Google Scholar 

  2. Cui Y, Dy JG, Alexander B, Jiang SB. Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines. Phys Med Biol. 2008;53:N315–27.

    Article  PubMed  Google Scholar 

  3. Cui Y, Dy JG, Sharp GC, Alexander B, Jiang SB. Robust fluoroscopic respiratory gating for lung cancer radiotherapy without implanted fiducial markers. Phys Med Biol. 2007;52:741–55.

    Article  PubMed  Google Scholar 

  4. Geraghty PR, Kee ST, McFarlane G, Razavi MK, Sze DY, Dake MD. CT-guided transthoracic needle aspiration biopsy of pulmonary nodules: needle size and pneumothorax rate. Radiology. 2003;229:475–81.

    Article  PubMed  Google Scholar 

  5. Jiang SB. Radiotherapy of mobile tumors. Semin Radiat Oncol. 2006;16:239–48.

    Article  CAS  PubMed  Google Scholar 

  6. Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol. 2004;14:81–90.

    Article  PubMed  Google Scholar 

  7. Keall PJ, Cattell H, Pokhrel D, Dieterich S, Wong KH, Murphy MJ, Vedam SS, Wijesooriya K, Mohan R. Geometric accuracy of a real-time target tracking system with dynamic multileaf collimator tracking system. Int J Radiat Oncol Biol Phys. 2006;65:1579–84.

    Article  PubMed  Google Scholar 

  8. Keall PJ, Mageras GS, Balter JM, Emery RS, Forster KM, Jiang SB, Kapatoes JM, Low DA, Murphy MJ, Murray BR, Ramsey CR, Van Herk MB, Vedam SS, Wong JW, Yorke E. The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys. 2006;33:3874–900.

    Article  PubMed  Google Scholar 

  9. Li R, Jia X, Lewis JH, Gu X, Folkerts M, Men C, Jiang SB. Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Med Phys. 2010;37:2822–6.

    Article  PubMed  Google Scholar 

  10. Li R, Lewis JH, Jia X, Gu X, Folkerts M, Men C, Song WY, Jiang SB. 3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy. Med Phys. 2011;38:2783–94.

    Article  PubMed  Google Scholar 

  11. Li R, Lewis JH, Jia X, Zhao T, Liu W, Wuenschel S, Lamb J, Yang D, Low DA, Jiang SB. On a PCA-based lung motion model. Phys Med Biol. 2011;56:6009–30.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Li R, Lewis JH, Jiang SB. Markerless fluoroscopic gating for lung cancer radiotherapy using generalized linear discriminant analysis. In: IEEE ICMLA. Miami, FL. 2009. p. 468–72.

    Google Scholar 

  13. Li R, Sharp G. Robust fluoroscopic tracking of fiducial markers: exploiting the spatial constraints. Phys Med Biol. 2013;58:1789–808.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Lin T, Cervino LI, Tang X, Vasconcelos N, Jiang SB. Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy. Phys Med Biol. 2009;54:981–92.

    Article  PubMed  Google Scholar 

  15. Lin T, Li R, Tang X, Dy JG, Jiang SB. Markerless gating for lung cancer radiotherapy based on machine learning techniques. Phys Med Biol. 2009;54:1555–63.

    Article  PubMed  Google Scholar 

  16. Machtay M, Bae K, Movsas B, Paulus R, Gore EM, Komaki R, Albain K, Sause WT, Curran WJ. Higher biologically effective dose of radiotherapy is associated with improved outcomes for locally advanced non-small cell lung carcinoma treated with chemoradiation: an analysis of the Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys. 2010;82(1):425–34.

    Article  PubMed  Google Scholar 

  17. Murphy MJ, Martin D, Whyte R, Hai J, Ozhasoglu C, Le QT. The effectiveness of breath-holding to stabilize lung and pancreas tumors during radiosurgery. Int J Radiat Oncol Biol Phys. 2002;53:475–82.

    Article  PubMed  Google Scholar 

  18. Perez CA, Bauer M, Edelstein S, Gillespie BW, Birch R. Impact of tumor control on survival in carcinoma of the lung treated with irradiation. Int J Radiat Oncol Biol Phys. 1986;12:539–47.

    Article  CAS  PubMed  Google Scholar 

  19. Perez CA, Stanley K, Rubin P, Kramer S, Brady L, Perez-Tamayo R, Brown GS, Concannon J, Rotman M, Seydel HG. A prospective randomized study of various irradiation doses and fractionation schedules in the treatment of inoperable non-oat-cell carcinoma of the lung. Preliminary report by the Radiation Therapy Oncology Group. Cancer. 1980;45:2744–53.

    Article  CAS  PubMed  Google Scholar 

  20. Popescu CC, Olivotto IA, Beckham WA, Ansbacher W, Zavgorodni S, Shaffer R, Wai ES, Otto K. Volumetric modulated arc therapy improves dosimetry and reduces treatment time compared to conventional intensity-modulated radiotherapy for locoregional radiotherapy of left-sided breast cancer and internal mammary nodes. Int J Radiat Oncol Biol Phys. 2010;76:287–95.

    Article  PubMed  Google Scholar 

  21. Seppenwoolde Y, Shirato H, Kitamura K, Shimizu S, van Herk M, Lebesque JV, Miyasaka K. Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. Int J Radiat Oncol Biol Phys. 2002;53:822–34.

    Article  PubMed  Google Scholar 

  22. Shah C, Grills IS, Kestin LL, McGrath S, Ye H, Martin SK, Yan D. Intrafraction variation of mean tumor position during image-guided hypofractionated stereotactic body radiotherapy for lung cancer. Int J Radiat Oncol Biol Phys. 2012;82:1636–41.

    Article  PubMed  Google Scholar 

  23. Shirato H, Seppenwoolde Y, Kitamura K, Onimura R, Shimizu S. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol. 2004;14:10–8.

    Article  PubMed  Google Scholar 

  24. Wulf J, Baier K, Mueller G, Flentje MP. Dose-response in stereotactic irradiation of lung tumors. Radiother Oncol. 2005;77:83–7.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305-5847, USA

    Ruijiang Li PhD

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  1. Ruijiang Li PhD
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Correspondence to Ruijiang Li PhD .

Editor information

Editors and Affiliations

  1. Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  2. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA

    Ruijiang Li

  3. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA

    Martin J. Murphy

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© 2015 Springer International Publishing Switzerland

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Li, R. (2015). Image-Based Motion Correction. In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi.org/10.1007/978-3-319-18305-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-18305-3_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18304-6

  • Online ISBN: 978-3-319-18305-3

  • eBook Packages: MedicineMedicine (R0)

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