Deep Learning for Magnetic Resonance Fingerprinting: a new Approach for Predicting Quantitative Parameter Values from time Series
Hoppe, E.; Körzdörfer, G.; Würfl, T.; Wetzl, J.; Lugauer, F.; Pfeuffer, J.; Maier, A.
Studies in Health Technology and Informatics 243: 202-206
2017
ISSN/ISBN: 1879-8365 PMID: 28883201 Document Number: 693285
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.