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Abstract #4402

Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks

Tae Hyung Kim1,2, Jaejin Cho1,2, Bo Zhao3, and Berkin Bilgic1,2,4
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Radiology, Harvard Medical School, Boston, MA, United States, 3Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, 4Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States


We introduce a novel framework that jointly performs advanced image reconstruction and model-based MR parameter mapping, where various traditional and modern reconstruction techniques and signal relaxation models (T1, T2, T2*, etc) can be integrated as a plug-and-play manner. Using the proposed framework, we also incorporated model-based parameter mapping with scan-specific deep learning reconstruction (a method named LORAKI). The experiment results with T2, T2* and T1 indicate that this synergistic combination is advantageous, providing improved quantitative imaging over existing methods, e.g. with up to 3.6-fold, 1.7-fold, 2.3-fold NRMSE gain in T2, T2* and T1 estimation, respectively.

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