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

Andrii Y Petrov; Mathieu Sellier; Paul D Docherty; J Geoffrey Chase

doi:10.1016/j.cmpb.2014.05.006

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

Comput Methods Programs Biomed. 2014 Oct;116(3):328-39. doi: 10.1016/j.cmpb.2014.05.006. Epub 2014 Jun 5.

Authors

Andrii Y Petrov¹, Mathieu Sellier², Paul D Docherty³, J Geoffrey Chase⁴

Affiliations

¹ Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: petrov.bme@gmail.com.
² Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: mathieu.sellier@canterbury.ac.nz.
³ Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: paul.docherty@canterbury.ac.nz.
⁴ Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: geoff.chase@canterbury.ac.nz.

PMID: 24986109
DOI: 10.1016/j.cmpb.2014.05.006

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.

Keywords: Brain; Inverse problem methods; Magnetic Resonance Elastography; Mechanical properties; Medical imaging; Tissue characterisation.

MeSH terms

Adult
Algorithms*
Brain / anatomy & histology*
Brain / physiology*
Computer Simulation
Elastic Modulus / physiology
Elasticity Imaging Techniques / methods*
Humans
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods*
Imaging, Three-Dimensional / methods*
Male
Models, Neurological*
Reproducibility of Results
Sensitivity and Specificity
Viscosity