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Abstract #2356

Off-resonance correction in MRF using Deep Learning in fingerprint space

Ronal Coronado1,2, Carlos Castillo-Passi1,2, Gabriel della Maggiora1,2, Sergio Manuel Uribe1,2, Cristian Tejos1, Claudia Prieto1,2,3, Cecilia Besa4, and Pablo Irarrazaval1,2
1Biomedical Imaging Center-Universidad Católica de Chile, Santiago, Chile, 2Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 3King's College London, London, United Kingdom, 4Departamento de Radiologia-Universidad Católica de Chile, Santiago, Chile


Magnetic Resonance Fingerprinting (MRF) acquisitions with balanced Steady State Free Precession (bSSFP) and spiral trajectories are prone to off-resonance artifacts. These artifacts affect the reconstruction of the tissue maps (T1 and T2). We propose to use a UNet CNN feed with fingerprints corrupted by off-resonance to generate corrected fingerprints with only aliasing in the bSSFP-MRF sequence. The feasibility of the proposed approach was evaluated in simulations and in-vivo brain data. Our method improved the NRMSE values for both quantitative maps T1 and T2. Considerably reducing the effects of the off-resonance by UNet-MRF in comparison to classical bSSFP-MRF.

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