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Abstract #3545

Synergistic reconstruction of undersampled multi-contrast MRI using weighted quadratic priors

ABOLFAZL MEHRANIAN1, Claudia Prieto1, Radhouene Neji1,2, Colm J. McGinnity3, Alexander J. Hammers3, and Andrew J. Reader1

1School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 2MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom, 3School of Biomedical Engineering and Imaging Sciences, King’s College London & Guy’s and St Thomas’ PET Centre, London, United Kingdom

We propose a simple and robust methodology for synergistic multi-contrast MR image reconstruction to improve image quality of undersampled MR data beyond what is achieved from conventional independent reconstruction methods. The advantages of the proposed methodology are threefold: i) it exploits quadratic priors that are mutually weighted using all available MR images, leading to preservation of unique features, ii) the weighting coefficients are independent of the relative signal intensity and contrast of different MR images and iii) the algorithm is based on a well-established parallel imaging iterative reconstruction, which makes the synergistic reconstruction of undersampled MR data clinically feasible

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