Repeatability and robustness of MP‐GRASP T₁ mapping

Abstract

To demonstrate the repeatability of fast 3D T 1 mapping using Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI and its robustness to variation of imaging parameters including flip angle and spatial resolution in phantoms and the brain. Theory and Methods:Multiple imaging experiments were performed to (1) assess the robustness of MP-GRASP T 1 mapping to B 1 inhomogeneity using a single tube phantom filled with uniform MnCl 2 liquid; (2) compare the repeatability of T 1 mapping between MP-GRASP and inversion recovery-based spin-echo (IR-SE; over 12 scans), using a commercial T1MES phantom; (3) evaluate the longitudinal variation of T 1 estimation using MP-GRASP with varying imaging parameters, including spatial resolution, flip angle, TR/TE, and acceleration rate, using the T1MES phantom (106 scans performed over a period of 12 months); and (4) evaluate the variation of T 1 estimation using MP-GRASP with varying imaging parameters in the brain (24 scans in a single visit). In addition, the accuracy of MP-GRASP T 1 mapping was also validated against IR-SE by performing linear correlation and calculating the Lin's concordance correlation coefficient (CCC).Results: MP-GRASP demonstrates good robustness to B 1 inhomogeneity, with intra-slice variability below 1% in the single tube phantom experiment. The longitudinal variability is good both in the phantom (below 2.5%) and in the brain (below 2%) with varying imaging parameters. The T 1 values estimated from MP-GRASP are accurate compared to that from the IR-SE imaging (R 2 = 0.997, Lin's CCC = 0.996). Conclusion: MP-GRASP shows excellent repeatability of T 1 estimation over time, andit is also robust to variation of different imaging parameters evaluated in this study.

Rapid fat–water separated T1 mapping using a single‐shot radial inversion‐recovery spoiled gradient recalled pulse sequence

“…Earlier studies have reported that this can be achieved with a model-based reconstruction. 24,25 It has also been shown that model-based reconstructions can become unstable for non-linear models 26 similar to the one presented in Equation (8) and should be linearized. To linearize a non-linear signal model such as the one shown in Equation ( 8), principal component analysis (PCA) can be used.…”

“…3 Further, due to the VFA method's known sensitivity towards excitation RF field (B 1 ) inhomogeneity, a separate B 1 measurement is usually required. 7 The Look-Locker method, on the other hand, is known to be insensitive to B 1 inhomogeneity, 8 but its original version requires a long acquisition time. 9 There is on-going research to enable clinically practical imaging time for the Look-Locker method.…”

“…The Look–Locker method, on the other hand, is known to be insensitive to B 1 inhomogeneity, 8 but its original version requires a long acquisition time 9 . There is on‐going research to enable clinically practical imaging time for the Look–Locker method.…”

Golden‐Angle Radial MRI: Basics, Advances, and Applications

“…In particular, the flexibility to provide varying temporal resolution with golden‐angle radial sampling can be exploited to reconstruct the same DCE‐MRI dataset for simultaneous qualitative assessment (with low temporal resolution) and quantitative perfusion analysis (with high temporal resolution). In addition, stack‐of‐stars golden‐angle radial sampling has also been demonstrated for imaging nonmoving organs in neuroimaging, 51,92 and it is also good for quantitative MRI, such as MR parameter mapping 48,51,93,94 …”

“…In addition, stack-of-stars golden-angle radial sampling has also been demonstrated for imaging nonmoving organs in neuroimaging, 51,92 and it is also good for quantitative MRI, such as MR parameter mapping. 48,51,93,94 Stack-of-stars golden-angle radial sampling is often compared against 3D golden-angle Cartesian sampling, in which the phase-encoding steps in the ky-kz plane can be segmented into different interleaves rotated by the golden angle. [95][96][97] The main advantage of the 3D golden-angle Cartesian trajectory is that all k-space data are sampled directly on a Cartesian grid, so that the time-consuming NUFFT is not needed for image reconstruction.…”