Abstract #0818

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

Marco Barbieri^1,2, Philip K. Lee³, Leonardo Brizi¹, Enrico Giampieri¹, Alexander R. Toews³, Gastone Castellani⁴, Daniel Remondini⁴, Brian A. Hargreaves^5,6,7, and Claudia Testa^8,9

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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