Name
Title | ||
|---|---|---|
Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series. |
Abstract | ||
|---|---|---|
The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state signal is generated by a pseudo-random excitation pattern. A comparison of the measured signal in each voxel with the physical model yields quantitative parameter maps. Currently, the comparison is done by matching a dictionary of simulated signals to the acquired signals. To accelerate the computation of quantitative maps we train a Convolutional Neural Network (CNN) on simulated dictionary data. As a proof of principle we show that the neural network implicitly encodes the dictionary and can replace the matching process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-808-2-202 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Convolutional Neural Networks,Deep Learning,Machine Learning,Magnetic Resonance Fingerprinting,Supervised Machine Learning | Voxel,Data mining,Pattern recognition,Convolutional neural network,Proof of concept,Artificial intelligence,Deep learning,Artificial neural network,Medicine,Computation,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
243 | 0926-9630 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabeth Hoppe | 1 | 1 | 2.05 |
| Gregor Körzdörfer | 2 | 1 | 0.36 |
| Tobias Würfl | 3 | 52 | 10.53 |
| Jens Wetzl | 4 | 1 | 0.70 |
| Felix Lugauer | 5 | 23 | 2.95 |
| Josef Pfeuffer | 6 | 39 | 9.50 |
| Andreas K. Maier | 7 | 560 | 178.76 |