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Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2* simultaneously with δf, T1, and T2

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TLDR
This study explores the possibility of using a gradient moment balanced sequence with a quadratically varied RF excitation phase in the magnetic resonance fingerprinting (MRF) framework to quantify T2* in addition to δf, T1, and T2 tissue properties.
Abstract
Purpose This study explores the possibility of using a gradient moment balanced sequence with a quadratically varied RF excitation phase in the magnetic resonance fingerprinting (MRF) framework to quantify T2 * in addition to δ f , T1 , and T2 tissue properties. Methods The proposed quadratic RF phase-based MRF method (qRF-MRF) combined a varied RF excitation phase with the existing balanced SSFP (bSSFP)-based MRF method to generate signals that were uniquely sensitive to δ f , T1 , T2 , as well as the distribution width of intravoxel frequency dispersion, Γ . A dictionary, generated through Bloch simulation, containing possible signal evolutions within the physiological range of δ f , T1 , T2 , and Γ , was used to perform parameter estimation. The estimated T2 and Γ were subsequently used to estimate T2 * . The proposed method was evaluated in phantom experiments and healthy volunteers (N = 5). Results The T1 and T2 values from the phantom by qRF-MRF demonstrated good agreement with values obtained by traditional gold standard methods (r2 = 0.995 and 0.997, respectively; concordance correlation coefficient = 0.978 and 0.995, respectively). The T2 * values from the phantom demonstrated good agreement with values obtained through the multi-echo gradient-echo method (r2 = 0.972, concordance correlation coefficient = 0.983). In vivo qRF-MRF-measured T1 , T2 , and T2 * values were compared with measurements by existing methods and literature values. Conclusion The proposed qRF-MRF method demonstrated the potential for simultaneous quantification of δ f , T1 , T2 , and T2 * tissue properties.

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Journal ArticleDOI

Erratum to “Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting”

TL;DR: A spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel is proposed and a unique two-step deep learning model is designed that learns the mapping from the observed signals to the desired properties for tissue quantification.

Fast group matching for MR fingerprinting reconstruction

TL;DR: A large dictionary of Bloch simulations is compared against rapidly acquired data to estimate tissue properties such as T1, T2, proton density, and B0, and this matching process can be a very computationally demanding portion of MRF reconstruction.
Journal ArticleDOI

Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations.

TL;DR: This review discusses the current implementations of MRF and their use in a clinical setting and highlights areas of need that must be addressed before MRF can be fully adopted into the clinic and makes recommendations to the MRF community on standardization and validation strategies.
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High-resolution 3D MR Fingerprinting using parallel imaging and deep learning.

TL;DR: Results of quantitative T1 and T2 maps demonstrate that improved tissue characterization can be achieved using the proposed method as compared to prior methods, and make high-resolution whole-brain quantitative MR imaging feasible for clinical applications.
Journal ArticleDOI

Magnetic resonance fingerprinting review part 2: Technique and directions.

TL;DR: This work highlights some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning.
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TL;DR: An approach to data acquisition, post-processing and visualization that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue is introduced—which is termed ‘magnetic resonance fingerprinting’ (MRF).
Journal ArticleDOI

Nonuniform fast Fourier transforms using min-max interpolation

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