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Intelligent Respiratory Motion Management for Radiation Therapy Treatment

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Machine and Deep Learning in Oncology, Medical Physics and Radiology

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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. 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. This chapter describes the nature of the motion-compensated treatment problem, the issues in using machine learning algorithms to handle it, and the variety of management algorithms that have been investigated and implemented.

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Authors and Affiliations

  1. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA

    Martin J. Murphy

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  1. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El Naqa

  2. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA

    Martin J. Murphy

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Murphy, M.J. (2022). Intelligent Respiratory Motion Management for Radiation Therapy Treatment. In: El Naqa, I., Murphy, M.J. (eds) Machine and Deep Learning in Oncology, Medical Physics and Radiology. Springer, Cham. https://doi.org/10.1007/978-3-030-83047-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-83047-2_14

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