Purpose To evaluate the feasibility of 23Na MR fingerprinting (MRF) for simultaneous quantification of T1, T2l∗, T2s∗, T2∗ in addition to ΔB0. Methods A framework for sodium relaxometry using MRF at 7T was developed, allowing simultaneous measurement of relaxation times and inhomogeneities in the static field. The technique distinguishes between bi‐ and monoexponential transverse relaxation and was validated in simulations with respect to the ground truth. In phantom measurements, a resolution of 2 × 2 × 12 mm3 was achieved within 1 h acquisition time, and the resulting parameter maps were compared to results from reference methods. Relaxation times in five healthy volunteers were measured with a resolution of 4 × 4 × 12 mm3. Results Phantom experiments revealed an agreement between the relaxation times obtained via 23Na‐MRF and the reference methods. In white matter, a longitudinal relaxation constant of T1 = 38.9 ± 4.8 ms was found, while values of T2l∗ = 29.2 ± 4.9 ms and T2s∗ = 4.7 ± 1.2 ms were found for the long and short component of the transverse relaxation. In cerebrospinal fluid, T1 was 67.7 ± 6.3 ms and T2∗ = 41.5 ± 3.4 ms. Conclusion This work demonstrates the feasibility of 23Na‐MRF for relaxometry in sodium MRI in both phantom and in vivo studies. Simultaneous quantification of T1, T2l∗, T2s∗, T2∗ and ΔB0 was possible within a 1 h measurement time.

Purpose To develop a framework for 3D sodium (23Na) MR fingerprinting (MRF), based on irreducible spherical tensor operators with tailored flip angle (FA) pattern and time‐efficient data acquisition for simultaneous quantification of T1, T2normall∗, T2normals∗, and T2∗ in addition to ΔB0. Methods 23Na‐MRF was implemented in a 3D sequence and irreducible spherical tensor operators were exploited in the simulations. Furthermore, the Cramér Rao lower bound was used to optimize the flip angle pattern. A combination of single and double echo readouts was implemented to increase the readout efficiency. A study was conducted to compare results in a multicompartment phantom acquired with MRF and reference methods. Finally, the relaxation times in the human brain were measured in four healthy volunteers. Results Phantom experiments revealed a mean difference of 1.0% between relaxation times acquired with MRF and results determined with the reference methods. Simultaneous quantification of the longitudinal and transverse relaxation times in the human brain was possible within 32 min using 3D 23Na‐MRF with a nominal resolution of (5 mm)3. In vivo measurements in four volunteers yielded average relaxation times of: T1,brain = (35.0 ± 3.2) ms, T2normall,brain∗ = (29.3 ± 3.8) ms and T2normals,brain∗ = (5.5 ± 1.3) ms in brain tissue, whereas T1,CSF = (61.9 ± 2.8) ms and T2,CSF∗ = (46.3 ± 4.5) ms was found in cerebrospinal fluid. Conclusion The feasibility of in vivo 3D relaxometric sodium mapping within roughly ½ h is demonstrated using MRF in the human brain, moving sodium relaxometric mapping toward clinically relevant measurement times.

This study aims to find a relation between the number of channels and the computational burden for specific absorption rate (SAR) calculation using virtual observation point-based SAR compression. Methods: Eleven different arrays of rectangular loops covering a cylinder of fixed size around the head of an anatomically correct voxel model were simulated. The resulting Q-matrices were compressed with 2 different compression algorithms, with the overestimation fixed to a certain fraction of worst-case SAR, median SAR, or minimum SAR. The latter 2 were calculated from 1e6 normalized random excitation vectors. Results:The number of virtual observation points increased with the number of channels to the power of 2.3-3.7, depending on the compression algorithm when holding the relative error fixed. Together with the increase in the size of the Q-matrices (and therefore the size of the virtual observation points), the total increase in computational burden with the number of channels was to the power of 4.3-5.7. Conclusion:The computational cost emphasizes the need to use the best possible compression algorithms when moving to high channel counts.