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arXiv · 1812.09560

Joint T1 and T2 Mapping with Tiny Dictionaries and Subspace-Constrained Reconstruction

Volkert Roeloffs·Martin Uecker·Jens Frahm
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Abstract

Purpose: To develop a method that adaptively generates tiny dictionaries for joint T1-T2 mapping. Theory: This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Methods: Data acquisition was accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. Results: All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Conclusion: Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.

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Volkert Roeloffs, Martin Uecker, Jens Frahm. 2018-12-22. Joint T1 and T2 Mapping with Tiny Dictionaries and Subspace-Constrained Reconstruction. https://doi.org/10.1109/tmi.2019.2939130

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