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Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapy

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Machine Learning in Radiation Oncology

Abstract

A number of treatment sites for external-beam radiation therapy, such as lung, breast, pancreatic, and liver cancers, move as the patient breathes, which compromises the precision of their irradiation. Modern radiation treatment modalities attempt to deal with this by adapting the radiation delivery to the respiratory motion as it occurs. This requires system control processes that can detect and anticipate respiratory movement patterns on a patient-by-patient basis in real time. Because breathing can be very idiosyncratic, this problem is a good candidate for machine learning algorithms that can be trained to model individual breathing patterns. Neural networks have proven quite effective in this capacity. This chapter describes the nature of the motion-compensated treatment problem and the issues in using a neural network to handle it.

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Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Virginia Commonwealth University, 401 College Street, 980058, Richmond, VA, 23298-0058, USA

    Martin J. Murphy

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  1. Martin J. Murphy
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Correspondence to Martin J. Murphy .

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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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Murphy, M.J. (2015). Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapy. 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_11

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  • DOI: https://doi.org/10.1007/978-3-319-18305-3_11

  • 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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