Abstract #2173

Subspace modeling enables time-efficient B1-corrected volumetric water-specific relaxometry with a long echo train CASPR trajectory

Jonathan Stelter¹, Kilian Weiss², Jakob Meineke³, Veronika Spieker^4,5, Weitong Zhang⁶, Julia A. Schnabel^4,5,7, Rickmer F. Braren¹, Bernhard Kainz^6,8, and Dimitrios C. Karampinos¹

¹School of Medicine and Health, Technical University of Munich, Munich, Germany, ²Philips GmbH Market DACH, Hamburg, Germany, ³Philips Research, Hamburg, Germany, ⁴Institute of Machine Learning for Biomedical Imaging, Helmholtz Munich, Neuherberg, Germany, ⁵School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, ⁶Department of Computing, Imperial College London, London, United Kingdom, ⁷School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, ⁸Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

Synopsis

Keywords: Relaxometry, Body

Motivation: The acquisition time of volumetric relaxometry techniques is constrained by the sampling efficiency and confounding factors such as fat or the B1 inhomogeneity.

Goal(s): To develop a method for volumetric T1- and T2-mapping that improves efficiency while accounting for confounding factors.

Approach: A Cartesian acquisition with spiral profile ordering is employed to increase sampling efficiency. A subspace reconstruction is proposed to perform B1- and water-specific T1- and T2-mapping considering the exact k-space sampling pattern.

Results: Simulation, phantom and in-vivo experiments were performed to evaluate the proposed method with regard to its quantification performance and its B1 sensitivity at 3T.

Impact: Subspace reconstruction in combination with a CASPR trajectory can improve the scan's efficiency and sampling flexibility while correcting for confounders such as fat or B1. The technique may be valuable to develop volumetric relaxometry in clinically acceptable scan times.

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