ArticleNMR in biomedicine2021
Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.
Article in NMR in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Fast MR signal simulations of microvascular and diffusion contributions using histogram-based approximation and recurrent neural networks.Magnetic resonance in medicine · 2025Article
- Enhancing MR vascular Fingerprinting with realistic microvascular geometries.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging.NMR in biomedicine · 2023Article
- MR fingerprinting of the prostate.Magma (New York, N.Y.) · 2022Review
- Artificial intelligence in cardiac magnetic resonance fingerprinting.Frontiers in cardiovascular medicine · 2022Review
- A unifying view on extended phase graphs and Bloch simulations for quantitative MRI.Scientific reports · 2021Article
- Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.NMR in biomedicine · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR fingerprinting. This work uses a new extended phase graph (EPG)-Bloch model for accurate simulation of transient-state, gradient-spoiled MR sequences, and proposes a recurrent neural network (RNN) as a fast surrogate of the EPG-Bloch model for computing large-scale MR signals and derivatives. The computational efficiency of the RNN model is demonstrated by comparisons with other existing models, showing one to three orders of acceleration compared with the latest GPU-accelerated, open-source EPG package. By using numerical and in vivo brain data, two used cases, namely, MRF dictionary generation and optimal experimental design, are also provided. Results show that the RNN surrogate model can be efficiently used for computing large-scale dictionaries of transient-state signals and derivatives within tens of seconds, resulting in several orders of magnitude acceleration with respect to state-of-the-art implementations. The practical application of transient-state quantitative techniques can therefore be substantially facilitated.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.