Magnetic Resonance Elastography Data Research Articles

Tomoelastography for Longitudinal Monitoring of Viscoelasticity Changes in the Liver and in Renal Allografts after Direct-Acting Antiviral Treatment in 15 Kidney Transplant Recipients with Chronic HCV Infection.

Besides the liver, hepatitis C virus (HCV) infection also affects kidney allografts. The aim of this study was to longitudinally evaluate viscoelasticity changes in the liver and in kidney allografts in kidney transplant recipients (KTRs) with HCV infection after treatment with direct-acting antiviral agents (DAAs). Fifteen KTRs with HCV infection were treated with DAAs (daclatasvir and sofosbuvir) for 3 months and monitored at baseline, end of treatment (EOT), and 3 (FU1) and 12 (FU2) months after EOT. Shear-wave speed (SWS) and loss angle of the complex shear modulus (φ), reflecting stiffness and fluidity, respectively, were reconstructed from multifrequency magnetic resonance elastography data with tomoelastography post-processing. After virus elimination by DAAs, hepatic stiffness and fluidity decreased, while kidney allograft stiffness and fluidity increased compared with baseline (hepatic stiffness change at FU1: −0.14 m/s, p < 0.01, and at FU2: −0.11 m/s, p < 0.05; fluidity at FU1: −0.05 rad, p = 0.04 and unchanged at FU2: p = 0.20; kidney allograft stiffness change at FU1: +0.27 m/s, p = 0.01, and at FU2: +0.30 m/s, p < 0.01; fluidity at FU1 and FU2: +0.06 rad, p = 0.02). These results suggest the restoration of mechanically sensitive structures and functions in both organs. Tomoelastography can be used to monitor the therapeutic results of HCV treatment non-invasively on the basis of hepatic and renal viscoelastic parameters.

Robustness of MR Elastography in the Healthy Brain: Repeatability, Reliability, and Effect of Different Reconstruction Methods.

Changes in brain stiffness can be an important biomarker for neurological disease. Magnetic resonance elastography (MRE) quantifies tissue stiffness, but the results vary between acquisition and reconstruction methods. To measure MRE repeatability and estimate the effect of different reconstruction methods and varying data quality on estimated brain stiffness. Prospective. Fifteen healthy subjects. 3T MRI, gradient-echo elastography sequence with a 50 Hz vibration frequency. Imaging was performed twice in each subject. Images were reconstructed using a curl-based and a finite-element-model (FEM)-based method. Stiffness was measured in the whole brain, in white matter, and in four cortical and four deep gray matter regions. Repeatability coefficients (RC), intraclass correlation coefficients (ICC), and coefficients of variation (CV) were calculated. MRE data quality was quantified by the ratio between shear waves and compressional waves. Median values with range are presented. Reconstruction methods were compared using paired Wilcoxon signed-rank tests, and Spearman's rank correlation was calculated between MRE data quality and stiffness. Holm-Bonferroni corrections were employed to adjust for multiple comparisons. In the whole brain, CV was 4.3% and 3.8% for the curl and the FEM reconstruction, respectively, with 4.0-12.8% for subregions. Whole-brain ICC was 0.60-0.74, ranging from 0.20 to 0.89 in different regions. RC for the whole brain was 0.14 kPa and 0.17 kPa for the curl and FEM methods, respectively. FEM reconstruction resulted in 39% higher stiffness than the curl reconstruction (P < 0.05). MRE data quality, defined as shear-compression wave ratio, was higher in peripheral regions than in central regions of the brain (P < 0.05). No significant correlations were observed between MRE data quality and stiffness estimates. MRE of the human brain is a robust technique in terms of repeatability. Caution is warranted when comparing stiffness values obtained with different techniques. 1 TECHNICAL EFFICACY STAGE: 1.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions. Statement of SignificanceThe study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computational brain model from experimental MRE data. The processed MRI, MRE, and tagged MRI data are publicly available at https://www.nitrc.org/projects/bbir/.

Viscoelasticity of children and adolescent brains through MR elastography

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G′ + iG″ with the fundamental MRE parameters being the storage modulus (G′), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

A stacked frequency approach for inhomogeneous time-dependent MRE: an inverse problem for the elastic shear modulus

Abstract We derive and analyse a new way to calculate the shear modulus of an inhomogeneous elastic material from time-dependent magnetic resonance elastography (MRE) measurements of its interior displacement. Even with such a rich data source, this is a challenging inverse problem because the coefficient of the shear modulus in the governing equations can be small (or potentially zero). Our approach overcomes this by combining different data sets into an overdetermined matrix–vector equation. It uses finite differences to approximate space derivatives and a Fourier interpolant in time, and we do not need to assume that the inhomogeneous material is ‘locally homogeneous’. Crucially, our construction ensures that the computed value of the (real) shear modulus is real: approximation methods based on the frequency domain version of the problem often give a complex shear modulus for the elastic case and this can be hard to interpret, especially if its imaginary part dominates. We carry out careful numerical tests on a one (space) dimensional analogue of the problem and on experimental MRE data for an inhomogeneous gel phantom.

Association between serum triglycerides and brain mechanical properties in humans

Dyslipidemia, including elevated triglycerides (TGs) and reduced high‐density lipoprotein cholesterol (HDL‐C), is a well‐established clinical risk factor for cardiovascular disease (CVD). Higher consumption of added sugar is associated with production of TGs and has been linked to elevated risk of CVD. Age‐related memory loss shares many of the same risk factors as CVD, especially in mid‐life; however, the underlying mechanisms remain incompletely understood. Accordingly, the aim of this study was to examine the independent associations among serum TGs and the TG/HDL‐C ratio with brain integrity and memory in cognitively normal young and middle‐aged (MA) adults. We hypothesized that elevated TGs would be associated with reduced brain integrity, particularly in the hippocampus which is essential to memory performance and that these associations would be linked to dietary consumption of added sugar. All measures were performed in 27 healthy young to MA adults (12 F/15 M, mean age: 42±15 y; age range: 22–69 y; mean BMI: 26±4 kg/m2; mean BP: 114±9/70±11 mmHg). TG and HDL‐C levels were assessed from a 12‐hour fasted blood draw. Brain integrity was measured from the mechanical viscoelastic properties of the global brain as well as the hippocampus (HC) using magnetic resonance elastography (MRE). MRE data were acquired using a Siemens 3T Magnetom Prisma MRI scanner to image shear waves generated via a pneumatic actuator (Resoundant, Rochester, MN) at 50Hz. Global brain stiffness (GBS) and bilateral HC stiffness were estimated from the viscoelastic shear stiffness maps derived from MRE displacement data using a nonlinear inversion algorithm. Delayed recall memory was assessed using the California Verbal Learning Test (CVLT). Dietary intake was quantified from participant self‐report over 3‐days using the Nutrition Data System for Research (NDSR) software. Associations among TG, TG/HDL‐C ratio, GBS and HC stiffness and memory recall were performed using partial correlations correcting for age. Associations among added sugar, TGs and brain stiffness were assessed using linear regression. HC stiffness tended to be inversely associated with serum TGs (r=−0.36, P=0.07) and the TG/HDL‐C ratio (r=−0.38, P=0.05). Likewise, delayed recall memory tended to be negatively associated with elevated serum TGs (r=−0.36, P=0.07) and was inversely correlated with the TG/HDL‐C ratio (r=−0.42, P=0.03). In contrast, there were no associations between GBS and either serum TGs (r=−0.1, P=0.62) or the TG/HDL‐C ratio (r=0.03, P=0.89). Serum TGs tended to be positively associated with higher dietary intake of added sugar (r=0.37, P=0.05); however, added sugar intake was only associated with GBS (r=−0.40, P=0.04) and was not significantly associated with the reduction in HC stiffness (r=−0.14, P=0.49). Higher intake of added sugar leads to elevated serum TGs, which may be associated with early loss of cognitive function by affecting the integrity of brain regions integral to memory performance. Future studies should explore the mechanisms by which elevated serum TGs contribute to the loss of HC integrity in mid‐life and examine how these events contribute to future risk for age‐related mild cognitive impairment.Support or Funding InformationNIH Grants P20GM103653, P20GM113125, K01AG054731

Bayesian Approach for Recovering Piecewise Constant Viscoelasticity from MRE Data

This paper deals with an inverse problem for recovering the piecewise constant viscoelasticity of a living body from MRE (Magnetic Resonance Elastography) data. Based on a scalar partial differential equation whose solution can approximately simulate MRE data, our inverse coefficient problem is considered as a statistical inverse problem of reconstructing the posterior distribution of unknown viscoelastic modulus. For sampling this distribution, one usually can use the Metropolis-Hastings Markov chain Monte Carlo (MH-MCMC) algorithm. However, without an appropriate “proposal” distribution given artificially, the MH-MCMC algorithm is hard to draw samples efficiently. To avoid this, a so-called slice sampling algorithm is introduced in this paper and applied for solving our problem. The performance of these statistical inversion algorithms is numerically tested basing on simulated data.

Immunotherapy response evaluation with magnetic resonance elastography (MRE) in advanced HCC

BackgroundCurrently, there are no imaging predictors of immunotherapy outcome in hepatocellular carcinoma (HCC). The study aim was to determine if stiffness changes measured by magnetic resonance elastography (MRE) can be a predictor of immunotherapy response in patients with advanced HCC.Materials and methodsThis was a prospective study of 15 patients with biopsy proven-advanced HCC treated with Pembrolizumab. All patients had liver MRE and liver biopsy at baseline and at 6 weeks of therapy. Change in HCC stiffness on MRE was compared with overall survival (OS), time to disease progression (TTP), and number of intratumoral CD3+ T lymphocytes. Analysis was performed using descriptive statistics and Spearman correlation (R); p-value < 0.05 was considered statistically significant.ResultsNine patients were evaluable. Median age was 71 years (range, 54–78). Etiology of liver disease was HCV (n = 4), HBV (n = 1) and NASH (n = 4). Median OS and TTP were 44 weeks and 13 weeks, respectively. Average baseline HCC stiffness and change in HCC stiffness were 5.0 kPa and 0.12 kPa, respectively. In contrast, average non-tumor liver stiffness was 3.2 kPa, and did not significantly change at 6 weeks (p = 0.42). Average size of measured tumor and change in size were 4 cm and − 0.32 cm, respectively. Change in HCC stiffness at 6 weeks correlated significantly with OS (R = 0.81), and TTP (R = 0.88,p < 0.01). Abundance of intratumoral T lymphocytes on tumor biopsy correlated significantly with HCC stiffness (R = 0.79,p = 0.007).ConclusionOur pilot MRE data suggests early change in tumor stiffness may be an indicator of immunotherapy response in patients with advanced HCC.

Association between large elastic artery stiffness and brain mechanical properties

Stiffening of the large elastic arteries (i.e., the aorta and carotid arteries) occurs with aging and may play a key role in memory impairment via a diminished pressure‐dampening effect, ultimately leading to loss of neuronal tissue integrity. In this regard, changes in brain tissue viscoelastic mechanical properties, an indirect measure of neuronal tissue integrity, may reflect the impact of elastic artery stiffening on brain health. Therefore, the purpose of this pilot study was to determine whether large elastic artery stiffness is associated with total brain viscoelasticity in healthy adults across the lifespan. All measures were performed in 13 healthy participants (6 females, mean age: 38 ± 15 y; age range: 22–69 y; mean BMI: 26 ± 5 kg/m2; mean BP: 111 ± 9/68 ± 10 mmHg). Carotid artery stiffness, beta‐stiffness index and compliance were assessed using B‐mode ultrasound (Logiq e, GE) coupled with applanation tonometry (SphygmoCor, AtCor Medical Inc.) and analyzed using commercial wall‐tracking software (Cardiovascular Suite, Quipu). Aortic stiffness was assessed as carotid‐to‐femoral pulse wave velocity (PWV) using applanation tonometry. Brain tissue viscoelastic mechanical properties (shear stiffness and damping ratio) were measured using magnetic resonance elastography (MRE). MRE data were acquired using 3T magnetic resonance imaging (Magnetom Prisma, Siemens, Inc.) while the brain viscoelastic property maps were generated using a nonlinear inversion algorithm. Associations between measures of age, large elastic artery stiffness and brain viscoelastic properties were determined using linear regression. PWV significantly increased with age (F = 25.6, p &lt; 0.05, r2 = 0.7) while total brain stiffness significantly decreased with age (F = 6.1, p &lt; 0.05, r2 = 0.36). Carotid artery stiffness (F = 4.3, p = 0.06, r2 = 0.28) and beta‐stiffness index (F = 2.9, p = 0.11, r2 = 0.21) tended to be inversely associated with total brain stiffness, whereas carotid artery compliance tended to be positively associated with total brain stiffness (F = 2.5, p = 0.14, r2 = 0.18). No relation was observed between PWV and total brain stiffness (F = 0.9, p = 0.37, r2 = 0.08). There were no associations between any measures of arterial stiffness and damping ratio. These preliminary data confirm that aging is associated with increase in arterial stiffness and loss of brain tissue integrity. They also indicate a possible association between large artery stiffening with brain tissue integrity and suggest that preserving elasticity of the carotid artery in particular may be important for protecting brain health with aging. Future work should assess the relation between carotid artery stiffness and brain mechanical properties in a larger group of individuals.Support or Funding InformationSupported by Center for Biomedical and Brain Imaging (CBBI) Pilot Grant P20GM103653 and Center of Biomedical Research Excellence (COBRE) Pilot Grant P20GM113125.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Magnetic resonance elastography in nonlinear viscoelastic materials under load

Characterisation of soft tissue mechanical properties is a topic of increasing interest in translational and clinical research. Magnetic resonance elastography (MRE) has been used in this context to assess the mechanical properties of tissues in vivo noninvasively. Typically, these analyses rely on linear viscoelastic wave equations to assess material properties from measured wave dynamics. However, deformations that occur in some tissues (e.g. liver during respiration, heart during the cardiac cycle, or external compression during a breast exam) can yield loading bias, complicating the interpretation of tissue stiffness from MRE measurements. In this paper, it is shown how combined knowledge of a material’s rheology and loading state can be used to eliminate loading bias and enable interpretation of intrinsic (unloaded) stiffness properties. Equations are derived utilising perturbation theory and Cauchy’s equations of motion to demonstrate the impact of loading state on periodic steady-state wave behaviour in nonlinear viscoelastic materials. These equations demonstrate how loading bias yields apparent material stiffening, softening and anisotropy. MRE sensitivity to deformation is demonstrated in an experimental phantom, showing a loading bias of up to twofold. From an unbiased stiffness of 4910.4 pm 635.8 Pa in unloaded state, the biased stiffness increases to 9767.5 pm ,1949.9 Pa under a load of approx 34% uniaxial compression. Integrating knowledge of phantom loading and rheology into a novel MRE reconstruction, it is shown that it is possible to characterise intrinsic material characteristics, eliminating the loading bias from MRE data. The framework introduced and demonstrated in phantoms illustrates a pathway that can be translated and applied to MRE in complex deforming tissues. This would contribute to a better assessment of material properties in soft tissues employing elastography.

Estimation of transversely isotropic material properties from magnetic resonance elastography using the optimised virtual fields method.

Magnetic resonance elastography (MRE) has been used to estimate isotropic myocardial stiffness. However, anisotropic stiffness estimates may give insight into structural changes that occur in the myocardium as a result of pathologies such as diastolic heart failure. The virtual fields method (VFM) has been proposed for estimating material stiffness from image data. This study applied the optimised VFM to identify transversely isotropic material properties from both simulated harmonic displacements in a left ventricular (LV) model with a fibre field measured from histology as well as isotropic phantom MRE data. Two material model formulations were implemented, estimating either 3 or 5 material properties. The 3-parameter formulation writes the transversely isotropic constitutive relation in a way that dissociates the bulk modulus from other parameters. Accurate identification of transversely isotropic material properties in the LV model was shown to be dependent on the loading condition applied, amount of Gaussian noise in the signal, and frequency of excitation. Parameter sensitivity values showed that shear moduli are less sensitive to noise than the other parameters. This preliminary investigation showed the feasibility and limitations of using the VFM to identify transversely isotropic material properties from MRE images of a phantom as well as simulated harmonic displacements in an LV geometry.

Magnetic resonance elastography inversions: Technical challenges and recent developments

Magnetic resonance elastography (MRE) is a phase contrast based MRI imaging technique that can quantitatively and non-invasively measure full 3D vector displacement data from propagating acoustic waves in vivo. The data acquired allow the calculation of local values of complex shear modulus and the generation of images that depict the viscoelastic properties of tissue. Acquisitions at different frequencies can capture the dispersive behavior of these quantities, and advanced approaches attempt to map anisotropic and poroelastic properties of materials. We will discuss current approaches for the inversion of MRE data, focusing on the issues and assumptions involved, and highlight some of the challenges faced in such inversions, particularly in thin or small structures in which wave propagation is dominated by waveguide effects. We will also present results based on neural network inversions, which may have lower repeatability error and be more resistant to noise than some current approaches.

Relative identifiability of anisotropic properties from magnetic resonance elastography.

Although magnetic resonance elastography (MRE) has been used to estimate isotropic stiffness in the heart, myocardium is known to have anisotropic properties. This study investigated the determinability of global transversely isotropic material parameters using MRE and finite-element modeling (FEM). A FEM-based material parameter identification method, using a displacement-matching objective function, was evaluated in a gel phantom and simulations of a left ventricular (LV) geometry with a histology-derived fiber field. Material parameter estimation was performed in the presence of Gaussian noise. Parameter sweeps were analyzed and characteristics of the Hessian matrix at the optimal solution were used to evaluate the determinability of each constitutive parameter. Four out of five material stiffness parameters (Young's modulii E1 and E3 , shear modulus G13 and damping coefficient s), which describe a transversely isotropic linear elastic material, were well determined from the MRE displacement field using an iterative FEM inversion method. However, the remaining parameter, Poisson's ratio, was less identifiable. In conclusion, Young's modulii, shear modulii and damping can theoretically be well determined from MRE data, but Poisson's ratio is not as well determined and could be set to a reasonable value for biological tissue (close to 0.5).

Acquisition and reconstruction conditions in silico for accurate and precise magnetic resonance elastography

Magnetic resonance elastography (MRE) is a non invasive imaging modality, which holds the promise of absolute quantification of the mechanical properties of human tissues in vivo. MRE reconstruction with algebraic inversion of the Helmholtz equation upon the curl of the shear displacement field may theoretically be flawless. However, its performances are challenged by multiple experimental parameters, especially the frequency and the amplitude of the mechanical wave, the voxel size and the signal-to-noise ratio of the MRE acquisition. A point source excitation was simulated and realistic displacement fields were analytically computed to simulate MRE data sets in an isotropic, homogeneous, linearly-elastic, and half-space infinite medium. Acquisition and reconstruction methods were challenged and the joint influence of the aforementioned parameters was studied. For a given signal-to-noise ratio, the conditions on the number of voxels per wavelength were determined for optimizing voxel-wise accuracy and precision in MRE. It was shown that, once data are acquired, the reconstruction quality could even be improved by effective interpolation or decimation so data could eventually fulfill favorable conditions for mechanical characterization of the tissue. Finally, the overall outcome, which is usually computed from the three acquired motion-encoded directions, may further be improved by appropriate averaging strategies that are based on adapted curl of shear displacement field quality-weighting.

A universal data quality assessment method for human brain magnetic resonance elastography using an elastic spherical phantom

Human brain Magnetic Resonance Elastography (MRE) is on the verge of being utilized as a clinical diagnostic tool. Several methods are being investigated at multiple clinical facilities; however each have different ways of introducing vibrations into the brain and processing image data. These differences result in dissimilar complex shear modulus values, which make it difficult to reproduce and report brain tissue mechanical properties across the research community and in longitudinal clinical studies. Human brain MRE Data Quality Assessment (DQA) reporting methods are needed. This study investigates a universal method to regulate the precision and accuracy of MRE independent of the vibration system and post processing technique. A thin-walled 15.2 cm diameter acrylic sphere was filled with silicone gel. The phantom is designed to mimic human head, neck, and torso mechanics during human brain MRE scanning and is compatible with most brain vibration methods. MRE-derived storage shear modulus yields approximately 1.5 kPa (60 Hz vibration) using a custom air pillow system (Resoundant, Inc.). The material will be cross validated with rheometry for further validation. This study provides insight on design characteristics for a standard DQA phantom for regulating the quality of human brain MRE for research reporting and in the clinic.

The Relationship of Three-Dimensional Human Skull Motion to Brain Tissue Deformation in Magnetic Resonance Elastography Studies.

In traumatic brain injury (TBI), membranes such as the dura mater, arachnoid mater, and pia mater play a vital role in transmitting motion from the skull to brain tissue. Magnetic resonance elastography (MRE) is an imaging technique developed for noninvasive estimation of soft tissue material parameters. In MRE, dynamic deformation of brain tissue is induced by skull vibrations during magnetic resonance imaging (MRI); however, skull motion and its mode of transmission to the brain remain largely uncharacterized. In this study, displacements of points in the skull, reconstructed using data from an array of MRI-safe accelerometers, were compared to displacements of neighboring material points in brain tissue, estimated from MRE measurements. Comparison of the relative amplitudes, directions, and temporal phases of harmonic motion in the skulls and brains of six human subjects shows that the skull-brain interface significantly attenuates and delays transmission of motion from skull to brain. In contrast, in a cylindrical gelatin "phantom," displacements of the rigid case (reconstructed from accelerometer data) were transmitted to the gelatin inside (estimated from MRE data) with little attenuation or phase lag. This quantitative characterization of the skull-brain interface will be valuable in the parameterization and validation of computer models of TBI.

Automated Liver Elasticity Calculation for 3D MRE.

Magnetic Resonance Elastography (MRE) is a phase-contrast MRI technique which calculates quantitative stiffness images, called elastograms, by imaging the propagation of acoustic waves in tissues. It is used clinically to diagnose liver fibrosis. Automated analysis of MRE is difficult as the corresponding MRI magnitude images (which contain anatomical information) are affected by intensity inhomogeneity, motion artifact, and poor tissue- and edge-contrast. Additionally, areas with low wave amplitude must be excluded. An automated algorithm has already been successfully developed and validated for clinical 2D MRE. 3D MRE acquires substantially more data and, due to accelerated acquisition, has exacerbated image artifacts. Also, the current 3D MRE processing does not yield a confidence map to indicate MRE wave quality and guide ROI selection, as is the case in 2D. In this study, extension of the 2D automated method, with a simple wave-amplitude metric, was developed and validated against an expert reader in a set of 57 patient exams with both 2D and 3D MRE. The stiffness discrepancy with the expert for 3D MRE was -0.8% ± 9.45% and was better than discrepancy with the same reader for 2D MRE (-3.2% ± 10.43%), and better than the inter-reader discrepancy observed in previous studies. There were no automated processing failures in this dataset. Thus, the automated liver elasticity calculation (ALEC) algorithm is able to calculate stiffness from 3D MRE data with minimal bias and good precision, while enabling stiffness measurements to be fully reproducible and to be easily performed on the large 3D MRE datasets.

A MRI-Compatible Combined Mechanical Loading and MR Elastography Setup to Study Deformation-Induced Skeletal Muscle Damage in Rats.

Deformation of skeletal muscle in the proximity of bony structures may lead to deep tissue injury category of pressure ulcers. Changes in mechanical properties have been proposed as a risk factor in the development of deep tissue injury and may be useful as a diagnostic tool for early detection. MRE allows for the estimation of mechanical properties of soft tissue through analysis of shear wave data. The shear waves originate from vibrations induced by an external actuator placed on the tissue surface. In this study a combined Magnetic Resonance (MR) compatible indentation and MR Elastography (MRE) setup is presented to study mechanical properties associated with deep tissue injury in rats. The proposed setup allows for MRE investigations combined with damage-inducing large strain indentation of the Tibialis Anterior muscle in the rat hind leg inside a small animal MR scanner. An alginate cast allowed proper fixation of the animal leg with anatomical perfect fit, provided boundary condition information for FEA and provided good susceptibility matching. MR Elastography data could be recorded for the Tibialis Anterior muscle prior to, during, and after indentation. A decaying shear wave with an average amplitude of approximately 2 μm propagated in the whole muscle. MRE elastograms representing local tissue shear storage modulus Gd showed significant increased mean values due to damage-inducing indentation (from 4.2 ± 0.1 kPa before to 5.1 ± 0.6 kPa after, p<0.05). The proposed setup enables controlled deformation under MRI-guidance, monitoring of the wound development by MRI, and quantification of tissue mechanical properties by MRE. We expect that improved knowledge of changes in soft tissue mechanical properties due to deep tissue injury, will provide new insights in the etiology of deep tissue injuries, skeletal muscle damage and other related muscle pathologies.

Increasing the spatial resolution and sensitivity of magnetic resonance elastography by correcting for subject motion and susceptibility-induced image distortions.

To improve the resolution of elasticity maps by adapting motion and distortion correction methods for phase-based magnetic resonance imaging (MRI) contrasts such as magnetic resonance elastography (MRE), a technique for measuring mechanical tissue properties in vivo. MRE data of the brain were acquired with echo-planar imaging (EPI) at 3T (n = 14) and 7T (n = 18). Motion and distortion correction parameters were estimated using the magnitude images. The real and imaginary part of the complex MRE data were corrected separately and recombined. The width of the point-spread function (PSF) and the position variability were calculated. The images were normalized to the Montreal Neurological Institute (MNI) anatomical template. The gray-to-white matter separability of the elasticity maps was tested. Motion correction sharpened the |G*| maps as demonstrated by a narrowing of the PSF by 0.78 ± 0.51 mm at 7T and 0.52 ± 0.63 mm at 3T. The amount of individual head motion during MRE acquisition correlated with the decrease in the width of the PSF at 7T (r = 0.53, P = 0.025) and at 3T (r = 0.69, P = 0.006) and with the increase of gray-to-white matter separability after motion correction at 7T (r = 0.64, P = 0.0039) and at 3T (r = 0.57, P = 0.0319). Improved spatial accuracy after distortion correction results in a significant increase in separability of gray and white matter stiffness (P = 0.0067), especially in inferior parts of the brain suffering from strong B0 inhomogeneities. We demonstrate that our method leads to sharper images and higher spatial accuracy, raising the prospect of the investigation of smaller brain areas with increased sensitivity in studies using MRE. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:134-141.

Quantification and comparison of 4D-flow MRI-derived wall shear stress and MRE-derived wall stiffness of the abdominal aorta.

Aortic wall shear stress (WSSFlow ) alters endothelial function, which in-turn changes aortic wall stiffness leading to remodeling in different disease states. Therefore, the aims of this study are to determine normal physiologic correlations between: (1) Magnetic Resonance Elastography (MRE)-derived aortic wall stiffness (WSMRE ) and WSSFlow ; (2) WSMRE and mean velocity; (3) WSMRE and pulse wave velocity (PWV);( 4) WSMRE and mean peak flow; and (5) WSMRE , WSSFlow and age using MRE and 4D-flow MRI in the abdominal aorta in healthy human subjects. Cardiac-gated aortic MRE and 4D-flow MRI data were acquired in 24 healthy volunteers using a 3 Tesla scanner. For MRE, 70 Hz external motion was applied to obtain wave images in all spatial directions in a separate breathhold. Whereas, 4D-flow data was acquired under free-breathing. Wave images in all the directions were processed to obtain three-dimensional-weighted stiffness map at end-systole (ES). WSSFlow , mean velocity, PWV and mean peak flow were obtained using 4D-flow data. Pearson correlation was performed to determine association between all variables. A significant negative correlation was observed between: (1) ES WSMRE and WSSFlow in both axial (r = -0.62; P = 0.006) and circumferential (r = -0.52; P = 0.016) directions; (2) ES WSMRE and mean velocity (r = -0.58; P = 0.012); and (3) age and WSSFlow in both axial (r = -0.71; P < 0.0001) and circumferential (r = -0.58; P = 0.0012) directions. A significant positive correlation was observed between: (1) ES WSMRE and PWV (r = 0.69; P < 0.0001); (2) ES WSMRE and mean peak flow (r = 0.53; P = 0.016); and (3) ES WSMRE and age (r = 0.63;P = 0.006). The negative significant correlation between aortic WSSFlow and WSMRE in normal volunteers demonstrates a relationship between WSMRE and WSSFlow . 2 J. Magn. Reson. Imaging 2017;45:771-778.