TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 1040
A1  - Song, P
A1  - Eldar, YC
A1  - Mazor, G
A1  - Rodrigues, MRD
SN  - 1520-6149
UR  - https://doi.org/10.1109/ICASSP.2019.8682622
EP  - 1044
AV  - public
Y1  - 2019/04/17/
TI  - Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network
KW  - Dictionaries
KW  -  Image restoration
KW  -  Image reconstruction
KW  -  Training
KW  -  Neural networks
KW  -  Table lookup
KW  -  Magnetic resonance imaging
KW  -  Magnetic Resonance Fingerprinting
KW  -  Quantitative Magnetic Resonance Imaging
KW  -  deep learning
KW  -  residual Convolutional Neural Network
T3  - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CY  - Brighton, UK
PB  - IEEE
ID  - discovery10085604
N2  - 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 Ũ 128 - 50Ũ 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.
ER  -