ArticleMagnetic resonance in medicine2023
Only-train-once MR fingerprinting for B
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
- Multi-molecular saturation-transfer MR fingerprinting for differentiating tumor progression from treatment-related change in post-treatment IDH-wildtype glioblastoma: a feasibility study.Journal of neuro-oncology · 2026Article
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 2026Article
- Technical Review of Magnetic Resonance Fingerprinting Applications in Cerebral Physiology.Magnetic resonance in medicine · 2026Review
- Optimization of Pulsed Saturation Transfer MR Fingerprinting (ST MRF) Acquisition Using the Cramér-Rao Bound and Sequential Quadratic Programming.Magnetic resonance in medicine · 2026Article
- Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models.Physics in medicine and biology · 2026Article
- Quantitative molecular imaging using deep magnetic resonance fingerprinting.Nature protocols · 2025Review
- Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification.Magnetic resonance in medicine · 2025Article
- Motion and magnetic field inhomogeneity correction techniques for chemical exchange saturation transfer (CEST) MRI: A contemporary review.NMR in biomedicine · 2025Review
- Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors.Magnetic resonance in medicine · 2024Article
- Self-supervised learning for denoising of multidimensional MRI data.Magnetic resonance in medicine · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network.Frontiers in radiology · 2024Article
- Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals.Scientific reports · 2023Article
- Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.Magnetic resonance in medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B
methodsAn only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B
resultsThe B
conclusionThe only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.
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Registered trials
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.