# RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting

arXiv: https://arxiv.org/abs/1907.05277 · HTML: https://mlsanity.com/paper/1907.05277/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting](https://mlsanity.com/paper/2107.08120/) (2021)
- [Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data](https://mlsanity.com/paper/2101.11442/) (2021)
- [MARVEL: MR Fingerprinting with Additional micRoVascular Estimates using bidirectional LSTMs](https://mlsanity.com/paper/2407.10512/) (2024)

## Abstract (arXiv)

Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed CNNs for the MRF reconstruction reducing the error in T1 and T2 by more than 80%.