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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