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Abstract #4620

Probing the sparsity of the MPnRAGE sequence through subspace compression

Natascha Niessen1,2, Tim Sprenger2,3, Carolin M. Pirkl2, Ana Beatriz Solana2, Matteo Cencini4, Enrico Avventi3,5, Michela Tosetti6, Marion I. Menzel2,7, and Julia A. Schnabel1,8,9
1School of Computation and Information Technology, Technical University of Munich, Munich, Germany, 2GE HealthCare, Munich, Germany, 3Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, 4INFN Pisa division, Pisa, Italy, 5Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden, 6IRCCS Stella Maris, Pisa, Italy, 7Technische Hochschule Ingolstadt, Ingolstadt, Germany, 8Helmholtz Munich, Munich, Germany, 9King’s College London, London, United Kingdom

Synopsis

Keywords: Image Reconstruction, Brain

Motivation: Acquiring multiple inversion contrasts in a single scan allows for comprehensive analysis of brain microstructure. However, the scan duration of a fully sampled MPnRAGE sequence is clinically infeasible.

Goal(s): Proving the potential for a more efficient MPnRAGE acquisition.

Approach: We assess the sparsity of MPnRAGE in the TI-space with data-driven subspace compression of a fully sampled Cartesian MPnRAGE acquisition with 10 TIs. We evaluate the feasibility of prospectively accelerated acquisitions using dictionary-based compressions.

Results: Data-driven subspace compression with two temporal basis yields images comparable to reconstructed fully sampled MPnRAGE images. Dictionary-based subspace compression requires three temporal basis for a similar quality.

Impact: We demonstrate the potential for an accelerated MPnRAGE acquisition by leveraging the sparsity in the TI-space. Rapid acquisition of different inversion contrasts will allow to analyze microstructure in a clinical context e.g. for the early detection of neurodegenerative diseases.

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