Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network

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Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network

Song, P; Eldar, YC; Mazor, G; Rodrigues, MRD; (2019) Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network. In: Proceedings of ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. pp. 1040-1044). IEEE: Brighton, UK. Green open access

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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.

Type:	Proceedings paper
Title:	Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network
Event:	ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Location:	Brighton, ENGLAND
Dates:	12 May 2019 - 17 May 2019
ISBN-13:	978-1-4799-8131-1
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/ICASSP.2019.8682622
Publisher version:	https://doi.org/10.1109/ICASSP.2019.8682622
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Dictionaries, Image restoration, Image reconstruction, Training, Neural networks, Table lookup, Magnetic resonance imaging, Magnetic Resonance Fingerprinting, Quantitative Magnetic Resonance Imaging, deep learning, residual Convolutional Neural Network
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10085604