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Author Liu, W. ♦ Sawant, A. ♦ Ruan, D.
Source United States Department of Energy Office of Scientific and Technical Information
Content type Text
Language English
Subject Keyword RADIOLOGY AND NUCLEAR MEDICINE ♦ RADIATION PROTECTION AND DOSIMETRY ♦ APPLIED LIFE SCIENCES ♦ ACCURACY ♦ BIOMEDICAL RADIOGRAPHY ♦ COMPUTERIZED TOMOGRAPHY ♦ ERRORS ♦ FORECASTING ♦ ITERATIVE METHODS ♦ LEARNING ♦ NMR IMAGING ♦ NONLINEAR PROBLEMS ♦ POLAR-CAP ABSORPTION
Abstract Purpose: The development of high dimensional imaging systems (e.g. volumetric MRI, CBCT, photogrammetry systems) in image-guided radiotherapy provides important pathways to the ultimate goal of real-time volumetric/surface motion monitoring. This study aims to develop a prediction method for the high dimensional state subject to respiratory motion. Compared to conventional linear dimension reduction based approaches, our method utilizes manifold learning to construct a descriptive feature submanifold, where more efficient and accurate prediction can be performed. Methods: We developed a prediction framework for high-dimensional state subject to respiratory motion. The proposed method performs dimension reduction in a nonlinear setting to permit more descriptive features compared to its linear counterparts (e.g., classic PCA). Specifically, a kernel PCA is used to construct a proper low-dimensional feature manifold, where low-dimensional prediction is performed. A fixed-point iterative pre-image estimation method is applied subsequently to recover the predicted value in the original state space. We evaluated and compared the proposed method with PCA-based method on 200 level-set surfaces reconstructed from surface point clouds captured by the VisionRT system. The prediction accuracy was evaluated with respect to root-mean-squared-error (RMSE) for both 200ms and 600ms lookahead lengths. Results: The proposed method outperformed PCA-based approach with statistically higher prediction accuracy. In one-dimensional feature subspace, our method achieved mean prediction accuracy of 0.86mm and 0.89mm for 200ms and 600ms lookahead lengths respectively, compared to 0.95mm and 1.04mm from PCA-based method. The paired t-tests further demonstrated the statistical significance of the superiority of our method, with p-values of 6.33e-3 and 5.78e-5, respectively. Conclusion: The proposed approach benefits from the descriptiveness of a nonlinear manifold and the prediction reliability in such low dimensional manifold. The fixed-point iterative approach turns out to work well practically for the pre-image recovery. Our approach is particularly suitable to facilitate managing respiratory motion in image-guide radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.
ISSN 00942405
Educational Use Research
Learning Resource Type Article
Publisher Date 2016-06-15
Publisher Place United States
Journal Medical Physics
Volume Number 43
Issue Number 6


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