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Abstract #1562

Magnetic Resonance Fingerprinting GAN-Transformer: removing off-resonance artifacts

Ronal Manuel Coronado1,2, Gabriel Manuel della Maggiora1,2, Carlos Manuel Castillo-Passi1,2, Gastão Cruz 3, Sergio Manuel Uribe1,2, Cristian Manuel Tejos1,2, Claudia Prieto2,3,4, and Pablo Manuel Irarrazaval2,4
1Centro de Imagenes Biomedicas-Universidad Catolica de Chile, Santiago, Chile, 2Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 3School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 4Centro de Imagenes Biomedicas- Pontificia Universidad Catolica 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. Thus, those artifacts hinder the reconstruction of the tissue maps (T1 and T2). In this work, we propose a model based on Generative Adversarial Networks (GANs) mixed with transformer blocks to decrease these artifacts. Our method improved the NMSE for both quantitative maps T1 and T2. Heavily reducing the effects of the off-resonance in comparison to classical bSSFP-MRF.

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