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Proceedings Paper

Shape-correlated deformation statistics for respiratory motion prediction in 4D lung
Author(s): Xiaoxiao Liu; Ipek Oguz; Stephen M. Pizer; Gig S. Mageras
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Paper 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.

Paper Details

Date Published: 23 February 2010
PDF: 10 pages
Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76252D (23 February 2010); doi: 10.1117/12.843974
Show Author Affiliations
Xiaoxiao Liu, The Univ. of North Carolina at Chapel Hill (United States)
Ipek Oguz, The Univ. of North Carolina at Chapel Hill (United States)
Stephen M. Pizer, The Univ. of North Carolina at Chapel Hill (United States)
Gig S. Mageras, Memorial Sloan-Kettering Cancer Ctr. (United States)

Published in SPIE Proceedings Vol. 7625:
Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; Michael I. Miga, Editor(s)

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