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Abstract #2055

A deep learning image based calibration model to predict motion using auxiliary sensors

Radhika Tibrewala1, Mahesh B Keerthivasan2, Kai Tobias Block1, Jan Paska1, and Ryan Brown1
1CAI2R, NYU School of Medicine, New York, NY, United States, 2Siemens Medical Solutions USA, New York, NY, United States


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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