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Abstract #4782

Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network

Pingfan Song1, Yonina C. Eldar2, Gal Mazor2, and Migue Rodrigues1

1Department of EE, University College London, London, United Kingdom, 2Department of EE, Technion, Israel Institute of Technology, Haifa, Israel

Dictionary matching based MR Fingerprinting (MRF) reconstruction approaches suffer from inherent quantization errors, as well as time-consuming parameter mapping operations that map temporal MRF signals to quantitative tissue parameters. To alleviate these issues, we design a residual convolutional neural network to capture the mappings from temporal MRF signals to tissue parameters. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. After training, our network is able to take a temporal MRF signal as input and directly output corresponding tissue parameters, playing the role of a dictionary and look-up table used in conventional approaches. However, the designed network outperforms conventional approaches in terms of both inference speed and reconstruction accuracy, which has been validated on both synthetic data and phantom data generated from healthy subjects.

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