Abstract
The fusion of the fast growing fields of imaging science, imaging hardware, and computational modeling, together with a broad interest in learning more about respiratory anatomy and physiology, has led to major advances in the scientific understanding and clinical applications of 4D motion modeling and radiotherapy. The advent of respiratory correlated, or ‘4D’ CT imaging has opened up applications in ventilation imaging, radiation oncology and beyond. In radiation oncology in particular, 4D CT is used in clinical practice by over 60 % of US cancer centers, growing \({\approx }7\) % per year. In this chapter we give discuss the history and growth of 4D CT, describe the variability in respiratory motion, and state both the applications and limitations of 4D CT imaging.
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Acknowledgments
The authors wish to thank Julie Baz for her formatting and always valuable critique. PJK acknowledges financial support of the Australian NHMRC Australia Fellowship and US NIH/NCI grants P01CA116602 and R01CA93626. Thanks to Drs. Daniel Simpson and Loren Mell, UC San Diego, for providing the raw data for Fig. 1.1.
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Keall, P., Yamamoto, T., Suh, Y. (2013). Introduction to 4D Motion Modeling and 4D Radiotherapy. In: Ehrhardt, J., Lorenz, C. (eds) 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy. Biological and Medical Physics, Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36441-9_1
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