MR-linac hybrid systems can dynamically image a tumor during radiotherapy to aid in a more precise delivery of the radiation dose. Motion tracking of the target is required and is currently performed by a deformable image registration on Cartesian bSSFP images. This study compares three different tracking methods (convolutional neuronal network, multi-template matching, and deformable image registration) to track a lung tumor in Cartesian images, where the performance of the three methods did not differ significantly. The convolutional neuronal network provided minimal decrease in tracking accuracy in a healthy volunteer when undersampled radial images were used to accelerate image acquisition.
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