Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190816 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Magnetic Resonance Fingerprinting,Magnetic Resonance Fingerprinting Reconstruction,Recurrent Neural Networks,Artificial Neural Networks | Pattern recognition,Computer science,Recurrent neural network,Artificial intelligence,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
267 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabeth Hoppe | 1 | 1 | 2.05 |
| Florian Thamm | 2 | 0 | 0.34 |
| Gregor Körzdörfer | 3 | 0 | 0.34 |
| Christopher Syben | 4 | 21 | 6.40 |
| Franziska Schirrmacher | 5 | 5 | 5.86 |
| Mathias Nittka | 6 | 0 | 1.01 |
| Josef Pfeuffer | 7 | 39 | 9.50 |
| Heiko Meyer | 8 | 0 | 0.34 |
| Andreas K. Maier | 9 | 560 | 178.76 |