A novel k-space learning based framework is introduced to compensate for bulk motion artifacts in UTE/ZTE radial acquisition schemes. The motion during the scan is modeled as rigid, and is parametrized by time varying translation and rotation parameters. The time varying parameters are used to define a forward model, which transforms the undistorted image to distorted k-t space data. The error between the measured and computed k-t space data is used to optimize the image and the deformation parameters using ADAM optimization. A multi-scale approach is used to minimize the computational complexity.
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