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Respiratory motion introduces significant uncertainty in radiotherapy planning for the thorax and upper abdomen. The primary goal of radiation therapy is to eliminate or reduce tumor cells while minimizing damage to surrounding healthy tissue, which is particularly challenging for lung tumors due to breathing-induced motion that can vary by several centimeters. Consequently, modeling respiratory motion has become essential in radiation therapy. Advanced 4D imaging techniques allow for the acquisition of spatiotemporal image sequences to explore dynamic processes within the patient's body. Image registration aids in estimating breathing-induced motion and describing the temporal changes in the position and shape of relevant structures by correlating images taken at different phases of the breathing cycle. These motion estimations help define precise treatment margins, calculate dose distributions, and develop prediction models for gated or robotic radiotherapy. This work 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 offers insights into current research and future perspectives, targeting biomedical engineers, medical physicis
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4D modeling and estimation of respiratory motion for radiation therapy, Jan Ehrhardt
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- Année de publication
- 2013
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