# Magnetic Resonance Fingerprinting using Recurrent Neural Networks

## TL;DR

This paper introduces a recurrent neural network approach for Magnetic Resonance Fingerprinting that improves reconstruction accuracy and significantly reduces computation time compared to traditional dictionary matching methods.

## Contribution

The paper presents a novel RNN-based method for MRF map reconstruction, enhancing scalability and efficiency over existing approaches.

## Key findings

- Achieves state-of-the-art T1 and T2 estimation accuracy.
- Reduces reconstruction time significantly.
- Outperforms existing neural network-based MRF methods.

## Abstract

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time-dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state-of-the-art estimates of T1 and T2 values. In addition, the reconstruction time is significantly reduced compared to dictionary-matching based approaches.