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Öksüz, I.* ; Cruz, G.* ; Clough, J.* ; Bustin, A.* ; Fuin, N.* ; Botnar, R.M.* ; Prieto, C.* ; King, A.P.* ; Schnabel, J.A.*

Magnetic resonance fingerprinting using recurrent neural networks.

In: (2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 08-11 April 2019, Venice, Italy). 2019. 1537-1540 (Proceedings - International Symposium on Biomedical Imaging ; 2019-April)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Gru ; Lstm ; Magnetic Resonance Fingerprinting ; Parameter Mapping ; Recurrent Neural Networks
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Konferzenzdatum 08-11 April 2019
Konferenzort Venice, Italy
Quellenangaben Band: 2019-April, Heft: , Seiten: 1537-1540 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)