title: Parametric-based brain Magnetic Resonance Elastography using a Rayleigh damping material model.
abstract: 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±0.11kPa and 2.2±0.11kPa, 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=333Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to μI=1000Pa, and ξd reconstruction with μI=333Pa 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.
authors.affiliation: Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: petrov.bme@gmail.com.
authors.forename: Andrii Y
authors.initials: AY
authors.lastname: Petrov
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: mathieu.sellier@canterbury.ac.nz.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Sellier
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: paul.docherty@canterbury.ac.nz.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Docherty
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: geoff.chase@canterbury.ac.nz.
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D055119:Elastic Modulus / Q000502:physiology; D054459:Elasticity Imaging Techniques / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014783:Viscosity
keywords: Brain; Inverse problem methods; Magnetic Resonance Elastography; Mechanical properties; Medical imaging; Tissue characterisation