# Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1807.06356 · HTML: https://mlsanity.com/paper/1807.06356/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [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)
- [Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction](https://mlsanity.com/paper/2506.23311/) (2025)
- [Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data](https://mlsanity.com/paper/2101.11442/) (2021)

## Abstract (arXiv)

Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionary-based approach.