• DocumentCode
    40095
  • Title

    Spatially-Resolved Hydraulic Conductivity Estimation Via Poroelastic Magnetic Resonance Elastography

  • Author

    Pattison, Adam Jeffry ; McGarry, Matthew ; Weaver, John B. ; Paulsen, Keith D.

  • Author_Institution
    Thayer Sch. of Eng., Dartmouth Coll., Hanover, NH, USA
  • Volume
    33
  • Issue
    6
  • fYear
    2014
  • fDate
    Jun-14
  • Firstpage
    1373
  • Lastpage
    1380
  • Abstract
    Poroelastic magnetic resonance elastography is an imaging technique that could recover mechanical and hydrodynamical material properties of in vivo tissue. To date, mechanical properties have been estimated while hydrodynamical parameters have been assumed homogeneous with literature-based values. Estimating spatially-varying hydraulic conductivity would likely improve model accuracy and provide new image information related to a tissue´s interstitial fluid compartment. A poroelastic model was reformulated to recover hydraulic conductivity with more appropriate fluid-flow boundary conditions. Simulated and physical experiments were conducted to evaluate the accuracy and stability of the inversion algorithm. Simulations were accurate (property errors were <; 2%) even in the presence of Gaussian measurement noise up to 3%. The reformulated model significantly decreased variation in the shear modulus estimate (p≪0.001) and eliminated the homogeneity assumption and the need to assign hydraulic conductivity values from literature. Material property contrast was recovered experimentally in three different tofu phantoms and the accuracy was improved through soft-prior regularization. A frequency-dependence in hydraulic conductivity contrast was observed suggesting that fluid-solid interactions may be more prominent at low frequency. In vivo recovery of both structural and hydrodynamical characteristics of tissue could improve detection and diagnosis of neurological disorders such as hydrocephalus and brain tumors.
  • Keywords
    Gaussian noise; biological tissues; biomechanics; biomedical MRI; elasticity; phantoms; shear modulus; Gaussian measurement noise; brain tumors; fluid-solid interactions; hydrocephalus; hydrodynamical material properties; in vivo tissue; mechanical properties; neurological disorder diagnosis; poroelastic magnetic resonance elastography; shear modulus; soft-prior regularization; spatially-resolved hydraulic conductivity estimation; tofu phantoms; Conductivity; Equations; Material properties; Mathematical model; Noise; Phantoms; Hydraulic conductivity; magnetic resonance (MR) elastography; poroelasticity; soft prior;
  • fLanguage
    English
  • Journal_Title
    Medical Imaging, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0062
  • Type

    jour

  • DOI
    10.1109/TMI.2014.2311456
  • Filename
    6774871