Quantitative effects of off‐resonance related distortion on brain mechanical property estimation with magnetic resonance elastography

Abstract

Off-resonance related geometric distortion can impact quantitative MRI techniques, such as magnetic resonance elastography (MRE), and result in errors to these otherwise sensitive metrics of brain health. MRE is a phase contrast technique to determine the mechanical properties of tissue by imaging shear wave displacements and estimating tissue stiffness through inverse solution of Navier's equation. In this study, we systematically examined the quantitative effects of distortion and corresponding correction approaches on MRE measurements through a series of simulations, phantom models, and in vivo brain experiments. We studied two different k-space trajectories, echo-planar imaging and spiral, and we determined that readout time, off-resonance gradient strength, and the combination of readout direction and off-resonance gradient direction, impact the estimated mechanical properties. Images were also processed through traditional distortion correction pipelines, and we found that each of the correction mechanisms works well for reducing stiffness errors, but are limited in cases of very large distortion. The ability of MRE to detect subtle changes to neural tissue health relies on accurate, artifactfree imaging, and thus off-resonance related geometric distortion must be considered when designing sequences and protocols by limiting readout time and applying correction where appropriate.

Correlated noise in brain magnetic resonance elastography

“…15 Imaged displacement fields are then used as inputs to an inversion algorithm to numerically estimate maps of mechanical properties. 16,17 Inversion algorithms are susceptible to image artifacts [18][19][20] and noise that can cause instability and errors in estimated properties, therefore, it is important to characterize performance under real-world noise conditions. 21,22 Noise is broadly defined as deviations from the desired signal of interest.…”

Evaluation of cerebral cortex viscoelastic property estimation with nonlinear inversion magnetic resonance elastography

“…Finally, we applied random Gaussian noise to the motion data to generate measurement uncertainty, though noise in brain MRE, data contains non-Gaussian contributions from physiological sources. At higher OSS-SNR levels, such as those used in this study, the impact of these different noise sources appears similar (Hannum et al, 2021;McIlvain et al, 2022), thus Gaussian noise is sufficient for the purposes of this study. Future work examining regional effects of local noise and other artifacts on cortical MRE regions is necessary.…”

Rapid MR elastography of the liver for subsecond stiffness sampling

“…In our experiments, we demonstrated the consistency of SWS values obtained by our novel MRE sequence in comparison with SE-EPI-MRE, which has become an established MRE technique for the liver. 25,38,39 SE-EPI-MRE is more susceptible to off-resonance-related distortion artifact than segmented spiral readout sequences, 40,41 whereas the latter are more susceptible to blurring artifacts. Other k-space sampling schemes for fast GRE MRE, such as radial readout, have been proposed and are currently being tested.…”