# MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

arXiv: https://arxiv.org/abs/1710.05267 · HTML: https://mlsanity.com/paper/1710.05267/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2017.

## Built on by (most cited first, 28 of 28)

- [Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance](https://mlsanity.com/paper/2203.11178/) (2022)
- [One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction](https://mlsanity.com/paper/2307.13220/) (2023)
- [A review and experimental evaluation of deep learning methods for MRI reconstruction](https://mlsanity.com/paper/2109.08618/) (2021)
- [PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction](https://mlsanity.com/paper/2306.11023/) (2023)
- [SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging](https://mlsanity.com/paper/2208.03904/) (2022)
- [CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping](https://mlsanity.com/paper/2210.06330/) (2022)
- [Accelerated and Quantitative 3D Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)](https://mlsanity.com/paper/2207.11297/) (2022)
- [Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging](https://mlsanity.com/paper/2310.04669/) (2023)
- [Unsupervised learning of MRI tissue properties using MRI physics models](https://mlsanity.com/paper/2107.02704/) (2021)
- [Cram'er-Rao bound-informed training of neural networks for quantitative MRI](https://mlsanity.com/paper/2109.10535/) (2021)
- [Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/2410.23318/) (2024)
- [Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning](https://mlsanity.com/paper/2411.06447/) (2024)
- [Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders](https://mlsanity.com/paper/2407.19866/) (2024)
- [StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations](https://mlsanity.com/paper/2408.02367/) (2024)
- [Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/2107.08120/) (2021)
- [Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data](https://mlsanity.com/paper/2311.01683/) (2023)
- [Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework](https://mlsanity.com/paper/2408.08376/) (2024)
- [Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI](https://mlsanity.com/paper/2507.11329/) (2025)
- [Accelerating multiparametric quantitative MRI using self-supervised scan-specific implicit neural representation with model reinforcement](https://mlsanity.com/paper/2508.00891/) (2025)
- [Modelling and optimization of nanovector synthesis for applications in drug delivery systems](https://mlsanity.com/paper/2112.02002/) (2021)
- [Exploiting Manifold Structured Data Priors for Improved MR Fingerprinting Reconstruction](https://mlsanity.com/paper/2310.05647/) (2023)
- [Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI](https://mlsanity.com/paper/2312.11468/) (2023)
- [MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs](https://mlsanity.com/paper/2407.10512/) (2024)
- [Adaptive Gate-Aware Mamba Networks for Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/2507.03369/) (2025)
- [Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)](https://mlsanity.com/paper/2602.03317/) (2026)
- [MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors](https://mlsanity.com/paper/2603.11316/) (2026)
- [Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning](https://mlsanity.com/paper/2606.02044/) (2026)
- [DeepPV-MRF: End-to-End Partial-Volume Fraction Estimation from MR Fingerprinting Time Series](https://mlsanity.com/paper/2610.19894/) (2026)

## Abstract (arXiv)

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.