Optimal Intermittent Measurements for Tumor Tracking in X-ray Guided Radiotherapy

摘要

In radiation therapy, tumor tracking is a challenging task that allows a better dose delivery. One practice isto acquire X-ray images in real-time during treatment, that are used to estimate the tumor location. Theseinformations are used to predict the close future tumor trajectory. Kalman prediction is a classical approachfor this task. The main drawback of X-ray acquisition is that it irradiates the patient, including its healthytissues. 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 whenthey are the most useful. Our aim is for a given budget of measurements to optimize the tracking process. Thisidea naturally brings to an optimal intermittent Kalman predictor for which measurement times are selectedto minimize the mean squared prediction error over the complete fraction. This optimization problem can besolved directly when the respiratory model has been identified and the optimal sampling times can be computedat once. These optimal measurement times are obtained by solving a combinatorial optimization problem usinga genetic algorithm. We created a test benchmark on trajectories validated on one patient. This new predictionmethod is compared to the regular Kalman predictor and a relative improvement of 9:8% is observed on the rootmean square position estimation error.