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TH‐CD‐207A‐07: Prediction of High Dimensional State Subject to Respiratory Motion: A Manifold Learning Approach
Author(s) -
Liu W,
Sawant A,
Ruan D
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4958176
Subject(s) - artificial intelligence , mathematics , nonlinear dimensionality reduction , subspace topology , dimensionality reduction , motion estimation , feature (linguistics) , pattern recognition (psychology) , kernel (algebra) , mean squared error , computer science , computer vision , algorithm , statistics , philosophy , linguistics , combinatorics
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.