MR fingerprinting Deep RecOnstruction NEtwork (DRONE).

PURPOSE

Demonstrate a novel fast method for reconstruction of multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods.

METHODS

A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed with the Extended Phase Graph formalism. The NN reconstruction accuracy for noiseless and noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in simulated numerical brain phantom data and ISMRM/NIST phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF FISP sequences with spiral readout. The utility of the method is demonstrated in a healthy subject at 1.5 T.

RESULTS

Network training required 10 to 74 minutes and once trained, data reconstruction required approximately 10 ms for the MRF EPI and 76 ms for the MRF FISP sequence. Reconstruction of simulated, noiseless brain data using the NN resulted in a root-mean-square error (RMSE) of 2.6 ms for T and 1.9 ms for T. The reconstruction error in the presence of noise was less than 10% for both T and T for signal-to-noise greater than 25 dB. Phantom measurements yielded good agreement (R=0.99/0.99 for MRF EPI T/T and 0.94/0.98 for MRF FISP T/T) between the T and T estimated by the NN and reference values from the ISMRM/NIST phantom.

CONCLUSION

Reconstruction of MRF data with a NN is accurate, 300–5000 fold faster and more robust to noise and undersampling than conventional MRF dictionary matching.