Trimmed autocalibrating K-space estimation based on structured matrix completion

Publication date: Available online 15 July 2017
Source:Magnetic Resonance Imaging
Author(s): Mark Bydder, Stanislas Rapacchi, Olivier Girard, Maxime Guye, Jean-Philippe Ranjeva
PurposeParallel imaging allows the reconstruction of undersampled data from multiple coils. This provides a means to reject and regenerate corrupt data (e.g. from motion artefact). The purpose of this work is to approach this problem using the SAKE parallel imaging method.Theory and methodsParallel imaging methods typically require calibration by fully sampling the center of k-space. This is a challenge in the presence of corrupted data, since the calibration data may be corrupted which leads to an errors-in-variables problem that cannot be solved by least squares or even iteratively reweighted least squares. The SAKE method, based on matrix completion and structured low rank approximation, was modified to detect and trim these errors from the data.ResultsSimulated and actual corrupted datasets were reconstructed with SAKE, the proposed approach and a more standard reconstruction method (based on solving a linear equation) with a data rejection criterion. The proposed approach was found to reduce artefacts considerably in comparison to the other two methods.ConclusionSAKE with data trimming improves on previous methods for reconstructing images from grossly corrupted data.



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