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Abstract #3754

Compressive Manifold Learning Respiratory Self-Gated Liver MRI: Estimating the Respiratory Motion Directly from Undersampled K-Space

Muhammad Usman1, Paul Aljabar1, Tobias Schaeffter1, Claudia Prieto1, 2

1King's College London, London, United Kingdom; 2Pontificia Universidad Catolica de Chile, Santiago, Chile

Manifold learning approaches can be applied in MRI to extract meaningful patterns of variation from the high-dimensional set of images. An example is respiratory self-gating where the low dimensional respiratory signal can be extracted from a set of free breathing images. In this work, we propose Compressive Manifold learning for respiratory self-gated MRI. This approach estimates the respiratory signal directly from undersampled k-space data, without the need for image reconstruction. Results from simulated free-breathing liver MR data show that the respiratory signal can be accurately extracted from highly undersampled k-space samples using the proposed method. Free-breathing acquisitions, performed prospectively in 3 volunteers, also demonstrate the feasibility of CML respiratory self-gating in k-space.