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Abstract #4362

Manifold Learning based ECG-free free breathing cardiac MRI for highly accelerated CINE

Muhammad Usman 1 , David Atkinson 2 , Tobias Schaeffter 1 , and Claudia Prieto 1,3

1 Division of Imaging Sciences and Biomedical Engineering, King's College London, London, Greater London, United Kingdom, 2 Department of Image Computing, University College London, London, United Kingdom, 3 Escuela de Ingenieria, Pontificia Universidad Catlica de Chile, Santiago, Chile

Manifold learning approaches can be applied in MRI to extract meaningful dimensions (manifolds) from the high-dimensional set of images. In this work, we propose a novel manifold learning based framework for cardiac and respiratory self-gating cardiac CINE MRI. Results show that the proposed approach estimates accurate cardiac and respiratory gating signals from ECG-free free breathing data and use these to achieve high spatial and temporal quality in retrospectively reconstructed CINE images.

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