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18 March 2016 Diaphragm motion characterization using chest motion data for biomechanics-based lung tumor tracking during EBRT
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Despite recent advances in image-guided interventions, lung cancer External Beam Radiation Therapy (EBRT) is still very challenging due to respiration induced tumor motion. Among various proposed methods of tumor motion compensation, real-time tumor tracking is known to be one of the most effective solutions as it allows for maximum normal tissue sparing, less overall radiation exposure and a shorter treatment session. As such, we propose a biomechanics-based real-time tumor tracking method for effective lung cancer radiotherapy. In the proposed algorithm, the required boundary conditions for the lung Finite Element model, including diaphragm motion, are obtained using the chest surface motion as a surrogate signal. The primary objective of this paper is to demonstrate the feasibility of developing a function which is capable of inputting the chest surface motion data and outputting the diaphragm motion in real-time. For this purpose, after quantifying the diaphragm motion with a Principal Component Analysis (PCA) model, correlation coefficient between the model parameters of diaphragm motion and chest motion data was obtained through Partial Least Squares Regression (PLSR). Preliminary results obtained in this study indicate that the PCA coefficients representing the diaphragm motion can be obtained through chest surface motion tracking with high accuracy.
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Elham Karami, Stewart Gaede, Ting-Yim Lee, and Abbas Samani "Diaphragm motion characterization using chest motion data for biomechanics-based lung tumor tracking during EBRT", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860Z (18 March 2016);

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