For MRI hindered by motion artifacts sensors are able to provide a surrogate for bulk motion, but may not be tissue specific. In this work, we use deep learning to build a motion model with an auxiliary pilot tone sensor during a fast-image calibration step. A neural network is used to learn the correlation between the pilot tone signal and the pixel-wise liver displacement which is predicted using an automatic segmentation model. The motion model is used to predict displacement on low frame rate images, thus offering the opportunity to perform motion resolved reconstruction.
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