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

2020 | journal article. A publication with affiliation to the University of Göttingen.

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​Roeloffs, Volkert, Martin Uecker, and Jens Frahm. "Joint T1 and T2 Mapping With Tiny Dictionaries and Subspace-Constrained Reconstruction​." ​IEEE Transactions on Medical Imaging, vol. 39, no. 4, ​2020, pp. 1008​-1014​, ​doi: 10.1109/TMI.2019.2939130. 

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Authors
Roeloffs, Volkert; Uecker, Martin ; Frahm, Jens 
Abstract
A novel method is developed that adaptively generates tiny dictionaries for joint T1-T2 mapping in magnetic resonance imaging. 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. Data acquisition is 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. All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.
Issue Date
2020
Journal
IEEE Transactions on Medical Imaging 
ISSN
0278-0062
eISSN
1558-254X
ISSN
0278-0062
eISSN
1558-254X
Language
English

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