MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Issue 3 (6th April 2018)

• Record Type: Journal Article
• Authors: Cohen, Ouri; Zhu, Bo; Rosen, Matthew S.
• Is Part Of: Magnetic resonance in medicine. Volume 80:Issue 3(2018)
• Page Start: 885
• Page End: 894
• Publication Date: 2018-04-06
• Subjects: deep learning -- DRONE -- EPI -- MR fingerprinting -- neural network -- optimization; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27198
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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 is quantified in simulated numerical brain phantom data and International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom data measured on 1.5T and 3T scanners with an optimized MRF EPI and MRF fast imaging with steady state precession (FISP) sequences with spiral readout. The utility of the method is demonstrated in a healthy subject in vivo at 1.5T. Results: Network training required 10 to 74 minutes; 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 RMS error (RMSE) of 2.6 ms for T1 and 1.9 ms for T2 . The reconstruction error in the presence of noise was less than 10% for both T1 and T2 for SNR greater than 25 dB. Phantom measurements yielded good agreement (R 2 = 0.99/0.99 for MRF EPI T1 /T2 and 0.94/0.98 for MRF FISP T1 /T2 ) between the T1 and T2 estimated by the NN and reference values from the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Conclusion: Reconstruction of MRF data with a NN is accurate, 300‐ to 5000‐fold faster, and more robust to noise and dictionary undersampling than conventional MRF dictionary‐matching.