Lung cancer radiotherapy is prone to errors due to uncertainties caused by the respiratory motion. If not accounted for, these errors may lead to poor radiation dose distribution, including insufficient does to the tumor volume and excessive dose to the healthy lung parenchyma. One effective method to account for respiratory motion is motion modeling. In this paper, we present a hybrid motion model which consists of two parts: 1) a computational biomechanical model of the lung for real-time tumor location/deformation estimation and 2) a Neural Network (NN) for real-time estimation of loading and boundary conditions of the lung biomechanical model. The second part uses the chest and abdomen surface motion as surrogate for the loading and boundary conditions, and is the main driver of the lung’s biomechanical model of the lung. In practice, the tumor location/deformation data estimated using the proposed motion model can be fed to actuators that guide a radiation therapy LINAC for continuous lung tumor targeting. The focus of this paper is two-fold: 1) developing two NNs for predicting the lung BC’s, including the diaphragm motion and transpulmonary pressure and 2) incorporating the NNs into a previously developed lung FE model to determine tumor location/deformation. Results of these two steps show highly favorable accuracy of the NNs in estimating the lung BC’s and highly favorable accuracy of the proposed motion model in predicting the lung tumor motion. As such, the proposed tracking approach can be potentially used for managing lung respiratory motion/deformation necessary for effective EBRT.
Elham Karami, Stewart Gaede, Ting-Yim Lee, and Abbas Samani, "A machine learning approach for biomechanics-based tracking of lung tumor during external beam radiation therapy," Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761C (Presented at SPIE Medical Imaging: February 15, 2018; Published: 13 March 2018); https://doi.org/10.1117/12.2293841.
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