Magnetic Resonance Elastography (MRE) is a noninvasive method for quantitatively assessing the viscoelastic properties of tissues, such as the brain. MRE has been successfully used to measure the material properties and diagnose diseases based on the difference in mechanical properties between diseased and normal tissue. However, MRE is still an emerging technology that is not part of routine clinical imaging like structural Magnetic Resonance Imaging (MRI), and the acquisition equipment is not widely available. Thus, it is challenging to collect MRE, but there is an increasing interest in it. In this study, we explore using structural MRI images to synthesize the MRE-derived material properties of the human brain. We use deep networks that employ both MRI and Diffusion Tensor Imaging (DTI) to explore the best input images for MRE image synthesis. This work is the first study to report on the feasibility of MRE synthesis from structural MRI and DTI.
This study explores reverberant shear wave elastography to create accurate magnetic resonance elastograms. The reverberant elastography technique utilizes the complex wave field originating from multiple point sources or reflected from various angles and superimposed with each other. The study was conducted on a calibrated brain phantom. Results showed that reverberant elastography produced accurate elastograms with an accuracy range of 84-97% and contrast-to-noise ratios of 24 dB, compared to an accuracy range of 86-97.7% and contrast-to-noise ratios of 25 dB for the established subzone inversion method.
Modeling brain tissue mechanics is important for understanding the pathogenesis of traumatic brain injury, with models often including brain tissue geometry and microstructural features like white matter fiber orientation. Recently, the cerebral vasculature has been included in models, however the effect of cerebral vessels on the mechanical response of the brain is unclear. A dataset of 23 subjects that includes structural MRI, angiography, and mechanical neuroimaging using magnetic resonance elastography (MRE) was collected to determine if there is a dependence of vasculature on in vivo brain mechanical properties. A pipeline was implemented using existing methods for processing anatomical, angiography, and MRE images; all images were co-registered for each subject and transformed to a common space. The regional mean stiffness and damping ratio of the brain, by anatomical segmentation, showed no dependence on vessel density but showed heterogeneity across the brain. A sub-regional analysis after stratifying by MRE stiffness showed a strong positive correlation in the cortical gray matter (R2=0.69) and a strong negative correlation in the deep gray matter (R2=0.76). Other regions showed similar trends with R2 values below 0.54. The opposite trends could be a result of regional microstructure difference, or a dependence on vessel type and size. A similar analysis using the brain damping ratio showed no dependence of vasculature on brain viscous properties. Quantifying the dependence of brain mechanical properties on vasculature will aid in understanding the biomechanics of the brain and inform their use in computational models of brain injury.
KEYWORDS: Magnetic resonance elastography, 3D modeling, Data modeling, Error analysis, Spatial frequencies, Tissues, Protactinium, Electronic filtering, Nickel, Signal to noise ratio
In magnetic resonance elastography (MRE), displacement fields from shear waves are inverted to estimate underlying material properties. Modulus differences detected by MRE may be used to distinguish tumors or other localized pathology in tissue. The accuracy of modulus estimates depends on the choice of the assumed constitutive model, as well as on the inversion algorithm, image resolution, and signal-to-noise ratio. In particular, in simpler inversion methods such as direct inversion and three-dimensional local frequency estimation (3D-LFE) the constitutive model is minimal (linear, elastic or viscoelastic, and isotropic) and the simplifying assumption of local homogeneity is usually made. The assumption of local homogeneity is often inaccurate [1], since the shear wavelength is typically comparable to the size of the structures of interest. Notably, the residual error (in direct inversion) between the model and the experimental data increases sharply at the boundaries of inclusions, while the “certainty” of the 3D-LFE estimate decreases. These error metrics may be used to detect local stiffness heterogeneity, as well as indicate variations in appropriate constitutive models. The utility of model uncertainty is demonstrated in simulations and with MRE data from a heterogeneous gel phantom.
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