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
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
We developed a method based on Generative Adversarial Networks to reduce the off-resonance artifacts in balanced Steady State Free Precession MR Fingerprinting.
T1 and T2 reconstructions from TR images with and without off-resonance correction.
Phase comparison between GAN correction and classical MRF with the ground truth reconstructions for different TRs.