Optimal Intermittent Measurements for Tumor Tracking in X-ray Guided Radiotherapy
For radiotherapy practitioners, this method reduces patient irradiation by optimizing X-ray acquisition times while maintaining tracking accuracy, but the improvement is incremental and validated on limited data.
The paper proposes an optimal intermittent Kalman predictor for tumor tracking in X-ray guided radiotherapy that selects measurement times to minimize prediction error under a fixed measurement budget, achieving a 9.8% relative improvement in root mean square position estimation error over regular Kalman prediction on a single patient's data.
In radiation therapy, tumor tracking is a challenging task that allows a better dose delivery. One practice is to acquire X-ray images in real-time during treatment, that are used to estimate the tumor location. These informations are used to predict the close future tumor trajectory. Kalman prediction is a classical approach for this task. The main drawback of X-ray acquisition is that it irradiates the patient, including its healthy tissues. In the classical Kalman framework, X-ray measurements are taken regularly, i.e. at a constant rate. In this paper, we propose a new approach which relaxes this constraint in order to take measurements when they are the most useful. Our aim is for a given budget of measurements to optimize the tracking process. This idea naturally brings to an optimal intermittent Kalman predictor for which measurement times are selected to minimize the mean squared prediction error over the complete fraction. This optimization problem can be solved directly when the respiratory model has been identified and the optimal sampling times can be computed at once. These optimal measurement times are obtained by solving a combinatorial optimization problem using a genetic algorithm. We created a test benchmark on trajectories validated on one patient. This new prediction method is compared to the regular Kalman predictor and a relative improvement of 9:8% is observed on the root mean square position estimation error.