Hoppe E, Thamm F, Körzdörfer G, Syben-Leisner C, Schirrmacher F, Nittka M, Pfeuffer J, Meyer H, Maier A (2019)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2019
Publisher: IOS Press
Edited Volumes: German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine
Series: Studies in Health Technology and Informatics
Book Volume: 267
Pages Range: 126-133
DOI: 10.3233/SHTI190816
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this limitation, deep learning based approaches, e.g. Convolutional Neural Networks (CNNs) have been proposed. In this work, we investigate the applicability of Recurrent Neural Networks (RNNs) for this reconstruction problem, as the signals are correlated in time. Compared to previous methods based on CNNs, RNN models yield significantly improved results using in-vivo data.
APA:
Hoppe, E., Thamm, F., Körzdörfer, G., Syben-Leisner, C., Schirrmacher, F., Nittka, M.,... Maier, A. (2019). Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks. In Rainer Röhrig, Harald Binder, Hans-Ulrich Prokosch, Ulrich Sax, Irene Schmidtmann, Susanne Stolpe, Antonia Zapf (Eds.), German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine. (pp. 126-133). IOS Press.
MLA:
Hoppe, Elisabeth, et al. "Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks." German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine. Ed. Rainer Röhrig, Harald Binder, Hans-Ulrich Prokosch, Ulrich Sax, Irene Schmidtmann, Susanne Stolpe, Antonia Zapf, IOS Press, 2019. 126-133.
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