Diaphragm motion characterization using chest motion data for biomechanics-based lung tumor tracking during EBRT

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.

[1]  P. Börnert,et al.  Free-breathing cardiac MR imaging: study of implications of respiratory motion--initial results. , 2001, Radiology.

[2]  Rafael Beyar,et al.  Prospective motion correction of X-ray images for coronary interventions , 2005, IEEE Transactions on Medical Imaging.

[3]  J Moseley,et al.  Contact surface and material nonlinearity modeling of human lungs , 2008, Physics in medicine and biology.

[4]  Abbas Samani,et al.  Toward in vivo lung's tissue incompressibility characterization for tumor motion modeling in radiation therapy. , 2013, Medical physics.

[5]  R. Mohan,et al.  Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. , 2003, Medical physics.

[6]  J Moseley,et al.  Sliding characteristic and material compressibility of human lung: parametric study and verification. , 2009, Medical physics.

[7]  Abbas Samani,et al.  A biomechanical approach for in vivo lung tumor motion prediction during external beam radiation therapy , 2015, Medical Imaging.

[8]  Tobias Schaeffter,et al.  Nonrigid Motion Modeling of the Liver From 3-D Undersampled Self-Gated Golden-Radial Phase Encoded MRI , 2012, IEEE Transactions on Medical Imaging.

[9]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[10]  Marco Riboldi,et al.  Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study , 2010, Technology in cancer research & treatment.

[11]  J. Jaldén,et al.  On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications. , 2005, Medical physics.

[12]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods/ J. A. Sethian , 1999 .