ReviewJournal of magnetic resonance imaging : JMRI2021
A Perspective on MR Fingerprinting.
Review in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 49 citations in OpenAlex.
- RF phase modulation improves quantitative transient state sequences under constrained conditions.Magma (New York, N.Y.) · 2026Article
- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- 3D multi-phase balanced non-steady-state free precession acquisition for multi-parameter mapping.Magma (New York, N.Y.) · 2025Article
- Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.IEEE transactions on bio-medical engineering · 2025Article
- Bias-reduced neural networks for parameter estimation in quantitative MRI.Magnetic resonance in medicine · 2024Article
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- Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.Magnetic resonance in medicine · 2024Article
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- Unconstrained quantitative magnetization transfer imaging: Disentangling TImaging neuroscience (Cambridge, Mass.) · 2024Article
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- Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint.Journal of magnetic resonance imaging : JMRI · 2023Article
- A fast MR fingerprinting simulator for direct error estimation and sequence optimization.Magnetic resonance imaging · 2023Article
- Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging.NMR in biomedicine · 2023Article
- Optimization of spin-lock times for TScientific reports · 2022Article
- Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques.Radiology · 2022Review
- Cramér-Rao bound-informed training of neural networks for quantitative MRI.Magnetic resonance in medicine · 2022Article
- Challenges in ensuring the generalizability of image quantitation methods for MRI.Medical physics · 2022Review
- Optimization of spin-lock times in TMagnetic resonance in medicine · 2022Article
- Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma.Magma (New York, N.Y.) · 2021Article
- Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.Neurologia medico-chirurgica · 2021Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
This article reviews the basic concept of MR fingerprinting (MRF) with the goal of highlighting MRF's key contributions, putting them in the context of other quantitative MRI literature, and refining MRF's terminology. The article discusses the robustness and flexibility of MRF's signature dictionary-matching reconstruction along with more advanced MRF reconstructions. A key feature of MRF is the lack of assumptions about the signal evolution, which gives scientists the flexibility to tailor sequences for their needs. The article argues that the concept of unique fingerprints does not capture the requirements for successful parameter mapping and that an analysis of the signal's derivatives with respect to biophysical parameters, such as relaxation times, is more informative, as it allows one to evaluate the efficiency of a pulse sequence. The article points at the source of MRF's efficiency, namely, flip angle variations at the time scale of the relaxation times, and reveals that MRF's advantages are strongest at long scan times, as required for 3D imaging. Further, it outlines how MRF's flexibility can be used to design mutually tailored pulse sequences and biophysical models with the goal of improving the reproducibility of parameter mapping biological tissue. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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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.