Magnetic resonance fingerprinting using recurrent neural networks
Oksuz I, Cruz G, Clough J, Bustin A, Fuin N, Botnar RM, Prieto C, King AP, Schnabel JA (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: IEEE Computer Society
Book Volume: 2019-April
Pages Range: 1537-1540
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Venice, ITA
ISBN: 9781538636411
DOI: 10.1109/ISBI.2019.8759502
Abstract
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and requires high computational time. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time-dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state-of-the-art estimates of T1 and T2 values. In addition, the reconstruction time is reduced compared to dictionary-matching based approach.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Oksuz, I., Cruz, G., Clough, J., Bustin, A., Fuin, N., Botnar, R.M.,... Schnabel, J.A. (2019). Magnetic resonance fingerprinting using recurrent neural networks. In Proceedings - International Symposium on Biomedical Imaging (pp. 1537-1540). Venice, ITA: IEEE Computer Society.
MLA:
Oksuz, Ilkay, et al. "Magnetic resonance fingerprinting using recurrent neural networks." Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, ITA IEEE Computer Society, 2019. 1537-1540.
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