Parametric-based brain Magnetic Resonance Elastography using a Rayleigh damping material model

• Rayleigh damping (RD) model is structurally non-identifiable with single frequency data without extensive a-priori information.
• We propose an alternative approach to overcome non-identifiability issue of the model.
• Parametric RD model was mooted in application to in vivo mechano brain imaging.
• Results indicate that parametric RD approach shows potential for diagnostic MRE imaging with single frequency data.

The three-parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (μI or ρI) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to in vivo brain imaging.Overall, success was achieved in reconstruction of the real shear modulus (μR) that showed good correlation with brain anatomical structures. The mean and standard deviation shear stiffness values of the white and gray matter were found to be 3 ± 0.11 kPa and 2.2 ± 0.11 kPa, respectively, which are in good agreement with values established in the literature or measured by mechanical testing. Parametric results with globally constrained μI indicate that selecting a reasonable value for the μI distribution has a major effect on the reconstructed ρI image and concomitant damping ratio (ξd). More specifically, the reconstructed ρI image using a realistic μI = 333 Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to μI = 1000 Pa, and ξd reconstruction with μI = 333 Pa accurately captured the higher damping levels expected within the vicinity of the ventricles.Parametric RD reconstruction shows potential for accurate recovery of the stiffness characteristics and overall damping profile of the in vivo living brain despite its underlying limitations. Hence, a parametric approach could be valuable with RD models for diagnostic MRE imaging with single frequency data.