ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2020
Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Transformer networks enable fast and robust dictionary generation for multiparametric cardiac mapping with variable timing.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2026Article
- Cardiac MR Fingerprinting at 0.55T Using a Deep Image Prior for Joint TJournal of magnetic resonance imaging : JMRI · 2026Article
- Clinical evaluation of free-breathing cardiac multi-parametric mapping using dictionary-based motion correction.Quantitative imaging in medicine and surgery · 2026Article
- Neural networks with personalized training for improved MOLLI TBMC medical imaging · 2025Article
- Fine-Tuning Deep Learning Model for Quantitative Knee Joint Mapping With MR Fingerprinting and Its Comparison to Dictionary Matching Method: Fine-Tuning Deep Learning Model for Quantitative MRF.NMR in biomedicine · 2025Article
- Recovery and Characterization of Tissue Properties from Magnetic Resonance Fingerprinting with Exchange.Journal of imaging · 2025Article
- Contrastive Learning for Accelerated MR Fingerprinting.Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition · 2025Article
- Whole pancreas water TMagma (New York, N.Y.) · 2025Article
- Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP.Scientific reports · 2025Article
- Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications.Journal of magnetic resonance imaging : JMRI · 2024Review
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- Joint MAPLE: Accelerated joint T1 and T2∗ mapping with scan-specific self-supervised networksMagnetic resonance in medicine · 2024Article
- Assessment of MRF for simultaneous TMagma (New York, N.Y.) · 2023Article
- Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network.Magnetic resonance in medicine · 2023Article
- Dynamic Cardiac Magnetic Resonance Fingerprinting During Vasoactive Breathing Maneuvers: First Results.Journal of cardiovascular imaging · 2023Article
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
- Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications.Current cardiology reports · 2023Review
- Motion-compensated TFrontiers in cardiovascular medicine · 2023Review
- MR fingerprinting of the prostate.Magma (New York, N.Y.) · 2022Review
- Cramér-Rao bound-informed training of neural networks for quantitative MRI.Magnetic resonance in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
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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.