The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T.
Spiral and ...rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T
and T
quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects.
In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T
and T
measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T
maps, T
maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm
within the acquisition time of 15 s.
It is feasible to measure T
and T
simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T
and T
quantification with no time penalty.
Objective
The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water–fat separation using rosette MRF at 0.55 T.
...Materials and methods
Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in
T
1
and
T
2
quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects.
Results
In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in
T
1
and
T
2
measurements. In addition, rosette MRF enables water–fat separation and can generate water- and fat- specific
T
1
maps,
T
2
maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm
3
within the acquisition time of 15 s.
Conclusion
It is feasible to measure
T
1
and
T
2
simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water–fat separation along with
T
1
and
T
2
quantification with no time penalty.
Purpose
To introduce a quantitative tool that enables rapid forecasting of T1 and T2 parameter map errors due to normal and aliasing noise as a function of the MR fingerprinting (MRF) sequence, which ...can be used in sequence optimization.
Theory and Methods
The variances of normal noise and aliasing artifacts in the collected signal are related to the variances in T1 and T2 maps through derived quality factors. This analytical result is tested against the results of a Monte‐Carlo approach for analyzing MRF sequence encoding capability in the presence of aliasing noise, and verified with phantom experiments at 3 T. To further show the utility of our approach, our quality factors are used to find efficient MRF sequences for fewer repetitions.
Results
Experimental results verify the ability of our quality factors to rapidly assess the efficiency of an MRF sequence in the presence of both normal and aliasing noise. Quality factor assessment of MRF sequences is in agreement with the results of a Monte‐Carlo approach. Analysis of MRF parameter map errors from phantom experiments is consistent with the derived quality factors, with T1 (T2) data yielding goodness of fit R2 ≥ 0.92 (0.80). In phantom and in vivo experiments, the efficient pulse sequence, determined through quality factor maximization, led to comparable or improved accuracy and precision relative to a longer sequence, demonstrating quality factor utility in MRF sequence design.
Conclusion
The here introduced quality factor framework allows for rapid analysis and optimization of MRF sequence design through T1 and T2 error forecasting.
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). ...While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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