MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

TL;DR

This paper introduces a deep learning-based method called DRONE for rapid, accurate reconstruction of multi-dimensional MR Fingerprinting data, significantly outperforming traditional techniques in speed and noise robustness.

Contribution

The paper presents a novel neural network approach for MRF data reconstruction that is faster, more accurate, and more noise-robust than conventional dictionary matching methods.

Findings

Reconstruction time reduced to approximately 10 ms.

RMSE for T1 and T2 significantly lower than traditional methods.

High agreement (R2=0.99) with reference phantom measurements.

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 using the Bloch equations. The accuracy of the NN reconstruction of noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in a both simulated numerical brain phantom data and acquired data from the ISMRM/NIST phantom. The utility of the method is demonstrated in a healthy subject in vivo at 1.5 T. RESULTS: Network training required 10 minutes and once trained, data reconstruction required approximately 10 ms. Reconstruction of simulated brain data using the NN resulted in a root-mean-square error (RMSE) of 3.5 ms for T1 and 7.8 ms for T2. The RMSE for the NN trained on sparse dictionaries was approximately 6 fold lower for T1 and 2 fold lower for T2 than conventional MRF dot-product dictionary matching on the same dictionaries. Phantom measurements yielded good agreement (R2=0.99) between the T1 and T2 estimated by the NN and reference values from the ISMRM/NIST phantom. CONCLUSION: Reconstruction of MRF data with a NN is accurate, 300 fold faster and more robust to noise and undersampling than conventional MRF dictionary matching.