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Abstract #0818

Deep Learning for Magnetic Resonance Fingerprinting: Data Augmentation with Phase Encoding and SVD Preprocessing for Accurate Parameter Reconstruction of FISP Data.

Marco Barbieri1,2, Philip K. Lee3, Leonardo Brizi1, Enrico Giampieri1, Alexander R. Toews3, Gastone Castellani4, Daniel Remondini4, Brian A. Hargreaves5,6,7, and Claudia Testa8,9

1Department of Physics and Astronomy, Univeristy of Bologna, Bologna, Italy, 2Department of Radiology, Stanford, Stanford, CA, United States, 3Department of Electrical Engineering, Stanford, Stanford, CA, United States, 4Department of Physics and Astronomy, University of Bologna, Bologna, Italy, 5Department of Radiology, Stanford University, Stanford, CA, United States, 6Department of Bioengineering, Stanford University, Stanford, CA, United States, 7Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 8University of Bologna, Bologna, Italy, 9Department of Biomedical and NeuroMotor Sciences, University of Bologna and Functional MRUnit, Policlinico S. Orsola - Malpighi, Bologna, Italy

Dictionary size limits the number of parameters one can aim to estimate with Magnetic Resonance Fingerprinting (MRF) Deep Neural networks (NN) have been recently proposed for MRF applications, both with numerical simulationsand with phantoms and in-vivo acquisitions. With real-valued NNs only the magnitude of the MRF signal has been considered as input. This choice releases from the need of considering the phase of the signal during training but can affect noise robustness and signal differentiation due to loss of information. In this work we propose a strategy to train a real valued NN that takes the real and imaginary parts of an MRF-FISP signal as input. We also propose to use SVD as preprocessing step for noise reduction. The presented results may help the developing of deep learning approaches for MRF, pushing fingerprinting pulse sequences design to add more meaningful MR parameters, such as diffusion, with no more limitations due to the dictionary size.

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