Quantifying stability of parameter estimates for in vivo nearly incompressible transversely-isotropic brain MR elastography

Abstract

Easily computable quality metrics for measured medical data at point-of-care are important for imaging technologies involving offline reconstruction. Accordingly, we developed a new data quality metric for in vivo transversely-isotropic (TI) magnetic resonance elastography (MRE) based on a generalization of the widely accepted octahedral shear-strain calculation. The met-ric uses MRE displacement data and an estimate of the TI property field to yield a “stability map” which predicts regions of low versus high accuracy in the resulting material property re-constructions. We can also calculate an average TI parameter stability (TIPS) score over all voxels in a region of interest for a given measurement to indicate how reliable the recovered mechanical property estimate for the region is expected to be. The calculation is rapid and places little demand on computing resources compared to the computationally intensive mate-rial property reconstruction from non-linear inversion (TI-NLI) of displacement fields, making it ideal for point-of-care evaluation of data quality. We test the predictions of the stability map for both simulated phantoms and in vivo human brain data. We used a range of different dis-placement datasets from vibrations applied in the anterior-posterior (AP), left-right (LR) and combined AP+LR directions. The TIPS and variability maps (noise sensitivity or variation from the mean of repeated MRE scans) were consistently anti-correlated. Notably, Spearman corre-lation coefficients |R|>0.6 were found between variability and TIPS score for individual white matter tracts with in vivo data. These observations demonstrate the reliability and promise of this data quality metric to screen data rapidly in realistic clinical MRE applications.

Mechanical Properties of White Matter Tracts in Aging Assessed via Anisotropic MR Elastography

“…Their anisotropic MRE approach is based on direct inversion with a large number of anisotropic parameters, which may induce uncertainty in estimated outcome measures. Conversely, our TI-NLI approach can effectively describe both the heterogeneity in properties and fiber direction and recovers only a minimal number of parameters, as described in previous studies (Jyoti et al, 2022; McGarry et al, 2022). Additionally, the use of TI-NLI with multi-excitation MRE has been well-characterized and shown to yield repeatable results in WM tracts (Smith et al, 2022).…”

Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography

“…In WM, estimates of shear anisotropy ( ϕ ) are in the range of 0.07–0.20, and tensile anisotropy ( ζ ) are between 0.3–0.5, which are similar to estimates in human WM [ 70 , 72 ], as well as to our previous estimates in minipigs [ 48 ]. The effective image resolution of each parameter is influenced by the shear wavelength, data resolution, data quality, and relative parameter sensitivity which together determine the regularization required to maintain stability of NLI [ 73 ]. Preliminary results with realistic simulated data suggest the effective resolution of μ is approximately 5 mm [ 24 ], whereas the damping ratio and anisotropy parameters have lower effective resolution of ~10 mm.…”