Magnetic Resonance Fingerprinting using Recurrent Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2018.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 2.
7 papers.
- 2021Cram'er-Rao bound-informed training of neural networks for quantitative MRIcited by 3
- 2021Learning Personal Representations from fMRIby Predicting Neurofeedback Performancecited by 2
- 2021Solving inverse problems with deep neural networks driven by sparse signal decomposition in a physics-based dictionarycited by 1
- 2023Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusioncited by 1
- 2021Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Datacited by 0
- 2021fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traitscited by 0
- 2023Exploiting Manifold Structured Data Priors for Improved MR Fingerprinting Reconstructioncited by 0
- 2023Exploiting Manifold Structured Data Priors for Improved MR Fingerprinting Reconstruction9 Oct 2023
- 2023Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion19 Apr 2023
- 2021fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits21 Dec 2021
- 2021Learning Personal Representations from fMRIby Predicting Neurofeedback Performance6 Dec 2021
- 2021Cram'er-Rao bound-informed training of neural networks for quantitative MRI22 Sep 2021
- 2021Solving inverse problems with deep neural networks driven by sparse signal decomposition in a physics-based dictionary16 Jul 2021
- 2021Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data26 Jan 2021