Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network

Abstract

Conventional dictionary matching based MR Fingerprinting (MRF) reconstruction approaches suffer from time-consuming operations that map temporal MRF signals to quantitative tissue parameters. In this paper, we design a 1-D residual convolutional neural network to perform the signature-to-parameter mapping in order to improve inference speed and accuracy. In particular, a 1-D convolutional neural network with shortcuts, a.k.a skip connections, for residual learning is developed using a TensorFlow platform. To avoid the requirement for a large amount of MRF data, the designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. The proposed approach was validated on both synthetic data and phantom data generated from a healthy subject. The reconstruction performance demonstrates a significantly improved speed – only 1.6s for reconstructing a pair of T1/T2 maps of size 128 × 128 – 50× faster than the original dictionary matching based method. The better performance was also confirmed by improved signal to noise ratio (SNR) and reduced root mean square error (RMSE). Furthermore, it is more compact to store a network instead of a large dictionary.