Matthew McGarry, Elijah Van Houten, Damian Sowinski, Dhrubo Jyoti, Daniel R Smith, Diego A Caban-Rivera, Grace McIlvain, Philip Bayly, Curtis L Johnson, John Weaver, Keith Paulsen
Author Information
Matthew McGarry: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA. Electronic address: matthew.d.mcgarry@dartmouth.edu.
Elijah Van Houten: Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.
Damian Sowinski: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
Dhrubo Jyoti: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
Daniel R Smith: University of Delaware, Newark, DE 19716, USA.
Diego A Caban-Rivera: University of Delaware, Newark, DE 19716, USA.
Grace McIlvain: University of Delaware, Newark, DE 19716, USA.
Philip Bayly: Washington University in St Louis, MO 63130, USA.
Curtis L Johnson: University of Delaware, Newark, DE 19716, USA.
John Weaver: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA; Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA.
Keith Paulsen: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA; Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA.
The white matter tracts of brain tissue consist of highly-aligned, myelinated fibers; white matter is structurally anisotropic and is expected to exhibit anisotropic mechanical behavior. In vivo mechanical properties of tissue can be imaged using magnetic resonance elastography (MRE). MRE can detect and monitor natural and disease processes that affect tissue structure; however, most MRE inversion algorithms assume locally homogenous properties and/or isotropic behavior, which can cause artifacts in white matter regions. A heterogeneous, model-based transverse isotropic implementation of a subzone-based nonlinear inversion (TI-NLI) is demonstrated. TI-NLI reconstructs accurate maps of the shear modulus, damping ratio, shear anisotropy, and tensile anisotropy of in vivo brain tissue using standard MRE motion measurements and fiber directions estimated from diffusion tensor imaging (DTI). TI-NLI accuracy was investigated with using synthetic data in both controlled and realistic settings: excellent quantitative and spatial accuracy was observed and cross-talk between estimated parameters was minimal. Ten repeated, in vivo, MRE scans acquired from a healthy subject were co-registered to demonstrate repeatability of the technique. Good resolution of anatomical structures and bilateral symmetry were evident in MRE images of all mechanical property types. Repeatability was similar to isotropic MRE methods and well within the limits required for clinical success. TI-NLI MRE is a promising new technique for clinical research into anisotropic tissues such as the brain and muscle.
