  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.
