Magnetic Resonance Fingerprinting

By Dan Ma, Vikas Gulani, Nicole Seiberlich, Kecheng Liu, Jeffrey L. Sunshine, Jeffrey L. Duerk, Mark A. Griswold. 2013

1,542 citations recorded by the source corpus.

Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization—which we term ‘magnetic resonance fingerprinting’ (MRF)—that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.

Subfields: Atomic and Molecular Physics, and Optics, Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “The processing of MRF after acquisition usually involves using various pattern recognition algorithms that try to match the fingerprints to a predefined dictionary of predicted signal evolutions,28 created using the Bloch equations [149,163].”
Better than real: Complex-valued neural nets for MRI fingerprinting (2017, methodology): “We compute proton density from the norm of the test signal as in [1].”
Estimation of Perfusion Properties with MR Fingerprinting Arterial Spin Labeling (2018, methodology): “In addition to sequence optimization, efficiency could also be improved with data undersampling with or without other reconstruction methods, which could also improve imaging speed [18,43].”
Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging (2018, neutral): “∑ λ = ∥ −Ω ∥ + ∥ ∥ + Ψ ∈ = + D D D D R d D arg min ( ) * ( ) (6) n N”
Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom (2018, methodology): “With the advent of methods such as magnetic resonance fingerprinting (122) and compressed sensing (123,124), quantitative MRI can now be performed in a clinically appropriate timeline; however, quality control with a quantitative MRI phantom is necessary to ensure the accuracy and precision of resul”
Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series (2017, methodology): “Previously presented methods for generating parameter maps in MRF are timeconsuming and require a dictionary of time series for every possible combination of parameters like T1 and T2 relaxation times [1, 2].”
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology (2018, background): “While many studies agree on the existence of 14 major nuclei, these can be subdivided histologically into many more subnuclei, so the exact number depends on the level of detail of the classification (Jones, 2012; Morel, 2007; Mai and Forutan, 2012).”
MR fingerprinting Deep RecOnstruction NEtwork (DRONE) (2017, methodology): “The mean6SD T1 and T2 for each tissue compartment are listed in Table 1 and are similar to values obtained from the literature.(1,31)”
MR Vascular Fingerprinting: A New Approach to Compute Cerebral Blood Volume, Mean Vessel Radius, and Oxygenation Maps in the Human Brain (2013, methodology): “A powerful alternative to analytical modeling has been recently proposed (Ma et al., 2013), in which numerical simulations of the MRI signal are used in conjunction with the concept of fingerprinting.”
What are normal relaxation times of tissues at 3 T? (2017, methodology): “In MRF, the sequence parameters (FA, TR and k-space trajectory) are varied in a pseudo-random fashionduring the acquisition togeneratefingerprints for each tissue (a unique signal which is a function of the multiple tissue properties under investigation) [5].”
Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI (2015, methodology): “An inversion recovery based steady-state free precession MR sequence is proposed, since this sequence type is particularly sensitive to changes in T1, T2, and off-resonance frequencies and provides the highest signal-to-noise ratio [6, 9].”