Magnetic resonance fingerprinting with quadratic RF phase for measurement of T2* simultaneously with δf, T1, and T2
Reads0
Chats0
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.read more
Citations
More filters
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
Stephen F. Cauley,Kawin Setsompop,Dan Ma,Yun Jiang,Huihui Ye,Mark A. Griswold,Elfar Adalsteinsson,Lawrence L. Wald +7 more
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.
Megan E. Poorman,Megan E. Poorman,Michele N. Martin,Dan Ma,Debra McGivney,Vikas Gulani,Mark A. Griswold,Kathryn E. Keenan +7 more
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.
Journal ArticleDOI
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.
Debra McGivney,Rasim Boyacioğlu,Yun Jiang,Yun Jiang,Megan E. Poorman,Megan E. Poorman,Nicole Seiberlich,Nicole Seiberlich,Vikas Gulani,Vikas Gulani,Kathryn E. Keenan,Mark A. Griswold,Dan Ma +12 more
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.
References
More filters
Journal ArticleDOI
A concordance correlation coefficient to evaluate reproducibility.
TL;DR: A new reproducibility index is developed and studied that is simple to use and possesses desirable properties and the statistical properties of this estimate can be satisfactorily evaluated using an inverse hyperbolic tangent transformation.
Journal ArticleDOI
Inorganic Nanoparticles for MRI Contrast Agents
TL;DR: Recent research has been conducted to develop nanoparticle‐based T1 contrast agents to overcome the drawbacks of iron oxide nanoparticles‐based negative T2 contrast agents.
Journal ArticleDOI
Transition metals, ferritin, glutathione, and ascorbic acid in parkinsonian brains.
Peter Riederer,Emin Sofic,Wolf-Dieter Rausch,Bruno Schmidt,Gavin P. Reynolds,Kurt A. Jellinger,Moussa B.H. Youdim +6 more
TL;DR: Reduced glutathione and the shift of the iron (II)/iron (III) ratio in favor of iron ( III) suggest that these changes might contribute to pathophysiological processes underlying PD.
Journal ArticleDOI
Magnetic resonance fingerprinting
Dan Ma,Vikas Gulani,Vikas Gulani,Nicole Seiberlich,Kecheng Liu,Jeffrey L. Sunshine,Jeffrey L. Duerk,Jeffrey L. Duerk,Mark A. Griswold,Mark A. Griswold +9 more
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
TL;DR: This paper presents an interpolation method for the nonuniform FT that is optimal in the min-max sense of minimizing the worst-case approximation error over all signals of unit norm and indicates that the proposed method easily generalizes to multidimensional signals.