Magnetic resonance imaging-guided linear particle accelerators use reconstructed images to adapt the radiation beam to the tumor location. Image-based approaches are relatively slow, causing healthy tissue to be irradiated upon subject movement. This study targets on the use of convolutional neural networks to estimate rigid patient movements directly from few acquired radial k-space lines. Thus, abrupt patient movements were simulated in image data of a head. Depending on the number of acquired spokes after movement, the network quantified this motion precisely. These first results suggest that neural network-based navigators can help accelerating beam guidance in radiotherapy.
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