Modeling and performance evaluation of a robotic treatment couch for tumor tracking

Tumor motion during radiation therapy increases the irradiation of healthy tissue. However, this problem may be mitigated by moving the patient via the treatment couch such that the tumor motion relative to the beam is minimized. The treatment couch poses limitations to the potential mitigation, thus the performance of the Protura (CIVCO) treatment couch was characterized and numerically modeled. The unknown parameters were identified using chirp signals and verified with one-dimensional tumor tracking. The Protura tracked chirp signals well up to 0.2 Hz in both longitudinal and vertical directions. If only the vertical or only the longitudinal direction was tracked, the Protura tracked well up to 0.3 Hz. However, there was unintentional yet substantial lateral motion in the former case. And during vertical motion, the extension caused rotation of the Protura around the lateral axis. The numerical model matched the Protura up to 0.3 Hz. Even though the Protura was designed for static positioning, it was able to reduce the tumor motion by 69% (median). The correlation coefficient between the tumor motion reductions of the Protura and the model was 0.99. Therefore, the model allows tumor-tracking results of the Protura to be predicted

PREDICTION OF RESPIRATORY MOTION

Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier