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Respiratory motion introduces significant uncertainty in radiotherapy planning for the thorax and upper abdomen. The primary aim of radiation therapy is to eliminate or reduce tumor cells while minimizing damage to surrounding healthy tissues by delivering a high radiation dose to the tumor and a lower dose to healthy organs. This challenge is particularly pronounced in lung tumors due to breathing-induced motion, which can vary by several centimeters. Consequently, modeling respiratory motion has become crucial in radiation therapy. Advanced 4D imaging techniques allow for the acquisition of spatiotemporal image sequences to study dynamic processes within the body. Image registration helps estimate breathing-induced motion and describes the temporal changes in the position and shape of relevant structures by correlating images from different breathing phases. These motion estimations inform the definition of accurate treatment margins, facilitate dose distribution calculations, and aid in developing prediction models for gated or robotic radiotherapy. The book illustrates the growing importance of image registration and motion estimation algorithms in interpreting complex 4D medical images. It presents various 4D CT acquisition techniques and motion estimation algorithms, demonstrating clinical relevance through example applications related to thoracic and abdominal tumor radiation therapy. Additionally, it provides insights
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4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, Jan Ehrhardt
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- Année de publication
- 2015
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