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Abstract #0867

High Fidelity Direct-Contrast Synthesis from Magnetic Resonance Fingerprinting in Diagnostic Imaging

Ke Wang1, Mariya Doneva2, Thomas Amthor2, Vera C. Keil3, Ekin Karasan1, Fei Tan4, Jonathan I. Tamir1,5, Stella X. Yu1,6, and Michael Lustig1
1Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, 2Philips Research, Hamburg, Germany, 3Universitätsklinikum Bonn, Bonn, Germany, 4Bioengineering, UC Berkeley-UCSF, San Francisco, CA, United States, 5Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, 6International Computer Science Institute, University of California, Berkeley, Berkeley, CA, United States

MR Fingerprinting is an emerging attractive candidate for multi-contrast imaging since it quickly generates reliable tissue parameter maps. However, contrast-weighted images generated from parameter maps often exhibit artifacts due to model and acquisition imperfections. Instead of direct modeling, we propose a supervised method to learn the mapping from MRF data directly to synthesized contrast-weighted images, i.e., direct contrast synthesis (DCS). In-vivo experiments on both volunteers and patients show substantial improvements of our proposed method over previous DCS method and methods that derive synthetic images from parameter maps.

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