Retrospective rigid-body motion correction methods often alternate between updating the estimate of the motion parameters and updating the image. The alternating minimization reconstruction is time-consuming, which is problematic in the clinical setting. We propose a new method for retrospective rigid-body motion correction that makes use of the insight that many shots of the imaging data have similar motion values, and therefore a reference image can be created from the imaging data itself. By leveraging this insight, this method is able to quickly reconstruct motion-corrected images without requiring large amounts of ML training data and without requiring an additional scout scan.
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