Shape-correlated deformation statistics for respiratory motion prediction in 4D lung

Xiaoxiao Liu; Ipek Oguz; Stephen M. Pizer; Gig S. Mageras

doi:10.1117/12.843974

23 February 2010 Shape-correlated deformation statistics for respiratory motion prediction in 4D lung

Xiaoxiao Liu, Ipek Oguz, Stephen M. Pizer, Gig S. Mageras

Proceedings Volume 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling; 76252D (2010) https://doi.org/10.1117/12.843974
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

4D image-guided radiation therapy (IGRT) for free-breathing lungs is challenging due to the complicated respiratory dynamics. Effective modeling of respiratory motion is crucial to account for the motion affects on the dose to tumors. We propose a shape-correlated statistical model on dense image deformations for patient-specic respiratory motion estimation in 4D lung IGRT. Using the shape deformations of the high-contrast lungs as the surrogate, the statistical model trained from the planning CTs can be used to predict the image deformation during delivery verication time, with the assumption that the respiratory motion at both times are similar for the same patient. Dense image deformation fields obtained by diffeomorphic image registrations characterize the respiratory motion within one breathing cycle. A point-based particle optimization algorithm is used to obtain the shape models of lungs with group-wise surface correspondences. Canonical correlation analysis (CCA) is adopted in training to maximize the linear correlation between the shape variations of the lungs and the corresponding dense image deformations. Both intra- and inter-session CT studies are carried out on a small group of lung cancer patients and evaluated in terms of the tumor location accuracies. The results suggest potential applications using the proposed method.

Citation Download Citation

Xiaoxiao Liu, Ipek Oguz, Stephen M. Pizer, and Gig S. Mageras "Shape-correlated deformation statistics for respiratory motion prediction in 4D lung", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76252D (23 February 2010); https://doi.org/10.1117/12.843974

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