We propose a deep learning-based respiratory navigator (DLnav) technique which uses a convolutional neuronal network (CNN) for respiratory motion detection. The pencil-beam navigator signals were transferred to the real time processing unit including a CNN module and a diaphragm position value was calculated there. DLnav was incorporated into prospectively navigator-gated 3D SPGR and its performance was evaluated in the volunteer scan. DLnav resulted in good synchronization with actual respiratory motion and reduced motion-induced blurring with two different tracker positions.
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