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Magnetic Resonance Imaging (MRI) is a widely used medical imaging modalityboasting great soft tissue contrast without ionizing radiation, butunfortunately suffers from long acquisition times. Long scan times can lead tomotion artifacts, for example due to bulk patient motion such as head movementand periodic motion produced by the heart or lungs. Motion artifacts candegrade image quality and in some cases render the scans nondiagnostic. Tocombat this problem, prospective and retrospective motion correction techniqueshave been introduced. More recently, data driven methods using deep neuralnetworks have been proposed. As a large number of publicly available MRIdatasets are based on Fast Spin Echo (FSE) sequences, methods that use them fortraining should incorporate the correct FSE acquisition dynamics.Unfortunately, when simulating training data, many approaches fail to generateaccurate motion-corrupt images by neglecting the effects of the temporalordering of the k-space lines as well as neglecting the signal decay throughoutthe FSE echo train. In this work, we highlight this consequence and demonstratea training method which correctly simulates the data acquisition process of FSEsequences with higher fidelity by including sample ordering and signal decaydynamics. Through numerical experiments, we show that accounting for the FSEacquisition leads to better motion correction performance during inference.

FSE Compensated Motion Correction for MRI Using Data Driven Methods