Head Movement Detection from Radial k-Space Lines using Convolutional Neural Networks - A Digital Phantom Study

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 con- volutional 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. De- pending on the number of acquired spokes after movement, the network quantiﰂed this motion precisely. These ﰂrst results suggest that neural network-based navigators can help accelerating beam guidance in radiotherapy.