The primary focus of the lab is the development of methodology for MRE in the pursuit to improve the ability of accurately and reliably map the mechanical properties of the human brain in vivo. We design MRI pulse sequences and image reconstruction algorithms to push the limits of spatial resolution while maintaining speed and SNR and limiting artifacts. We couple our approaches with the nonlinear inversion (NLI) algorithm, developed by our colleagues at Dartmouth College, to provide high-quality brain MRE results.
Ongoing research directions include:
- High-resolution sampling schemes: We seek to continue to push the limits of achievable spatial resolution in MRE through the development of SNR efficient sampling schemes that build upon our previous platforms in multishot and multislab spiral imaging, but also gains from novel hardware capabilities of the Siemens Prisma and 64-channel head coil.
- Motion-induced phase error correction: We are developing methods to characterize and correct for motion-induced phase errors that corrupt the MRE displacement data. These errors arise from spurious vibration and physiological noise, and require a properly designed navigator to estimate during image reconstruction.
- Accelerated subspace-based sampling: Through modeling of the MRE sampling space, we are developing schemes to accelerate scan times by taking advantage of data redundancy. Such schemes will allow us to image at greater resolution to reach more challenging targets and also improve the clinical adoptability of our current sequences.
- Approaches for anisotropic MRE: This project is focused on characterizing the anisotropic properties of white matter tracts in the brain and developing methodology to reconstruct these properties in the MRE framework. We are developing actuation, imaging, and inversion approaches to appropriately sample and handle displacement data required.
CL Johnson, et al, “Brain MR Elastography with Multiband Excitation and Nonlinear Motion-Induced Phase Error Correction,” ISMRM 2016.
AT Anderson, et al, “Effect of Nonlinear Inversion Parameters on Brain MR Elastography,” IMECE 2015.
AT Anderson, et al “Observation of Direction-Dependent Mechanical Properties in the Human Brain with Multi-Excitation MR Elastography,” Journal of the Mechanical Behavior of Biomedical Materials, 2016.
MDJ McGarry, et al, “Suitability of Poroelastic and Viscoelastic Mechanical Models for High and Low Frequency MR Elastography,” Medical Physics, 2015.
D Klatt, et al, “Simultaneous, Multidirectional Acquisition of Displacement Fields in Magnetic Resonance Elastography of the in vivo Human Brain,” Journal of Magnetic Resonance Imaging, 2015.
CL Johnson, et al, “Accelerating MR Elastography with Sparse Sampling and Low-Rank Reconstruction,” ISMRM 2014.
CL Johnson, et al, “3D Multislab, Multishot Acquisition for Fast, Whole-Brain MR Elastography with High Signal-to-Noise Efficiency,” Magnetic Resonance in Medicine, 2014.
MDJ McGarry, et al, “Including Spatial Information in Nonlinear Inversion MR Elastography Using Soft Prior Regularization,” IEEE Transactions on Medical Imaging, 2013.
CL Johnson, et al, “Magnetic Resonance Elastography of the Brain Using Multishot Spiral Readouts with Self-Navigated Motion Correction,” Magnetic Resonance in Medicine, 2013.
MDJ McGarry, et al, “Multiresolution MR Elastography Using Nonlinear Inversion,” Medical Physics, 2012.