Multifrequency inversion in magnetic resonance elastography

Measurement of biomechanical properties of transversely isotropic biological tissue using traveling wave expansion

The anisotropic mechanical properties of fiber-embedded biological tissues are essential for understanding their development, aging, disease progression, and response to therapy. However, accurate and fast assessment of mechanical anisotropy in vivo using elastography remains challenging. To address the dilemma of achieving both accuracy and efficiency in this inverse problem involving complex wave equations, we propose a computational framework that utilizes the traveling wave expansion model. This framework leverages the unique wave characteristics of transversely isotropic material and physically meaningful operator combinations. The analytical solutions for inversion are derived and engineering optimization is made to adapt to actual scenarios. Measurement results using simulations, ex vivo muscle tissue, and in vivo human white matter validate the framework in determining in vivo anisotropic biomechanical properties, highlighting its potential for measurement of a variety of fiber-embedded biological tissues.

Rapid Stiffness Mapping in Soft Biologic Tissues With Micrometer Resolution Using Optical Multifrequency Time-Harmonic Elastography

Rapid mapping of the mechanical properties of soft biological tissues from light microscopy to macroscopic imaging can transform fundamental biophysical research by providing clinical biomarkers to complement in vivo elastography. This work introduces superfast optical multifrequency time-harmonic elastography (OMTHE) to remotely encode surface and subsurface shear wave fields for generating maps of tissue stiffness with unprecedented detail resolution. OMTHE rigorously exploits the space-time propagation characteristics of multifrequency time-harmonic waves to address current limitations of biomechanical imaging and elastography. Key solutions are presented for stimulation, wave decoding, and stiffness reconstruction of shear waves at multiple harmonic frequencies, all tuned to provide consistent stiffness values across resolutions from microns to millimeters. OMTHE's versatility is demonstrated by simulations, phantoms, Bacillus subtilis biofilms, zebrafish embryos and adult zebrafish, reflecting the diversity of biological systems from a mechanics perspective. By zooming in on stiffness details from coarse to finer scales, OMTHE has the potential to advance mechanobiology and offers a way to perform biomechanics-based tissue histology that consistently matches in vivo time-harmonic elastography in patients.

Combining Multifrequency Magnetic Resonance Elastography With Automatic Segmentation to Assess Renal Function in Patients With Chronic Kidney Disease

BACKGROUND

Multifrequency MR elastography (mMRE) enables noninvasive quantification of renal stiffness in patients with chronic kidney disease (CKD). Manual segmentation of the kidneys on mMRE is time-consuming and prone to increased interobserver variability.

PURPOSE

To evaluate the performance of mMRE combined with automatic segmentation in assessing CKD severity.

STUDY TYPE

Prospective.

PARTICIPANTS

A total of 179 participants consisting of 95 healthy volunteers and 84 participants with CKD.

FIELD STRENGTH/SEQUENCE

3 T, single shot spin echo planar imaging sequence.

ASSESSMENT

Participants were randomly assigned into training (n = 58), validation (n = 15), and test (n = 106) sets. Test set included 47 healthy volunteers and 58 CKD participants with different stages (21 stage 1-2, 22 stage 3, and 16 stage 4-5) based on estimated glomerular filtration rate (eGFR). Shear wave speed (SWS) values from mMRE was measured using automatic segmentation constructed through the nnU-Net deep-learning network. Standard manual segmentation was created by a radiologist. In the test set, the automatically segmented renal SWS were compared between healthy volunteers and CKD subgroups, with age as a covariate. The association between SWS and eGFR was investigated in participants with CKD.

STATISTICAL TESTS

Dice similarity coefficient (DSC), analysis of covariance, Pearson and Spearman correlation analyses. P < 0.05 was considered statistically significant.

RESULTS

Mean DSCs between standard manual and automatic segmentation were 0.943, 0.901, and 0.970 for the renal cortex, medulla, and parenchyma, respectively. The automatically quantified cortical, medullary, and parenchymal SWS were significantly correlated with eGFR (r = 0.620, 0.605, and 0.640, respectively). Participants with CKD stage 1-2 exhibited significantly lower cortical SWS values compared to healthy volunteers (2.44 ± 0.16 m/second vs. 2.56 ± 0.17 m/second), after adjusting age.

CONCLUSION

mMRE combined with automatic segmentation revealed abnormal renal stiffness in patients with CKD, even with mild renal impairment.

PLAIN LANGUAGE SUMMARY

The renal stiffness of patients with chronic kidney disease varies according to the function and structure of the kidney. This study integrates multifrequency magnetic resonance elastography with automated segmentation technique to assess renal stiffness in patients with chronic kidney disease. The findings indicate that this method is capable of distinguishing between patients with chronic kidney disease, including those with mild renal impairment, while simultaneously reducing the subjectivity and time required for radiologists to analyze images. This research enhances the efficiency of image processing for radiologists and assists nephrologists in detecting early-stage damage in patients with chronic kidney disease.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

The contributions of relative brain viscosity to brain function and health

Magnetic resonance elastography has emerged over the last two decades as a non-invasive method for quantitatively measuring the mechanical properties of the brain. Since the inception of the technology, brain stiffness has been the primary metric used to describe brain microstructural mechanics. However, more recently, a secondary measure has emerged as both theoretical and experimental significance, which is the ratio of tissue viscosity relative to tissue elasticity. This viscous-to-elastic ratio describes different but complementary aspects of brain microstructural health and is theorized to relate to microstructural organization, as opposed to stiffness, which is related to tissue composition. The relative viscosity of brain tissue changes regionally during maturation, aging and neurodegenerative disease. It also exhibits unique characteristics in brain tumours and hydrocephalus, and is of interest for characterizing traumatic head impacts. Most notably, regional measures of relative brain tissue viscosity appear to hold a unique role in describing cognitive function. For instance, in young adults, relatively lower hippocampal viscosity compared to elasticity repeatedly and sensitively relates to spatial, declarative and verbal memory performance. Importantly, these same trends are not found with hippocampal stiffness, or hippocampal volume, highlighting a potential sensitivity of relative viscosity to underlying cellularity that contributions to normal healthy brain function. Likewise in young adults, in the orbitofrontal cortex, lower relative viscosity relates to better performance on fluid intelligence tasks, and in the Broca's area of children ages 5-7, lower relative viscosity is indicative of better language performance. In these instances, this ratio shows heightened sensitivity over other structural MRI metrics, and importantly, provides a quantitative and intrinsic alternative to measuring structure-function relationships with task-based fMRI. There are ongoing efforts to improve the accuracy and repeatability of the relative viscosity measurement, and much work is needed to reveal the cellular underpinning of changes to tissue viscosity. But it appears clear that regionally measuring the viscous-to-elastic ratio holds the potential to noninvasively reveal an aspect of tissue microstructure that is clinically, cognitively and functionally relevant to our understanding of brain function and health.

Magnetic resonance elastography in a nutshell: Tomographic imaging of soft tissue viscoelasticity for detecting and staging disease with a focus on inflammation

Magnetic resonance elastography (MRE) is an emerging clinical imaging modality for characterizing the viscoelastic properties of soft biological tissues. MRE shows great promise in the noninvasive diagnosis of various diseases, especially those associated with soft tissue changes involving the extracellular matrix, cell density, or fluid turnover including altered blood perfusion - all hallmarks of inflammation from early events to cancer development. This review covers the fundamental principles of measuring tissue viscoelasticity by MRE, which are based on the stimulation and encoding of shear waves and their conversion into parameter maps of mechanical properties by inverse problem solutions of the wave equation. Technical challenges posed by real-world biological tissue properties such as viscosity, heterogeneity, anisotropy, and nonlinear elastic behavior of tissues are discussed. Applications of MRE measurement in both humans and animal models are presented, with emphasis on the detection, characterization, and staging of diseases related to the cascade of biomechanical property changes from early to chronic inflammation in the liver and brain. Overall, MRE provides valuable insights into the biophysics of soft tissues for imaging-based detection and staging of inflammation-associated tissue changes.

A Versatile MR Elastography Research Tool with a Modified Motion Signal-to-noise Ratio Approach

PURPOSE

This study aimed to facilitate research progress in MR elastography (MRE) by providing a versatile and convenient application for MRE reconstruction, namely the MRE research tool (MRE-rTool). It can be used for a series of MRE image analyses, including phase unwrapping, arbitrary bandpass and directional filtering, noise assessment of the wave propagation image (motion SNR), and reconstruction of the elastogram in both 2D and 3D MRE acquisitions. To reinforce the versatility of MRE-rTool, the conventional method of motion SNR was modified into a new method that reflects the effects of image filtering.

METHODS

MRE tests of the phantom and liver were performed using different estimation algorithms for stiffness value (algebraic inversion of the differential equation [AIDE], local frequency estimation [LFE] in MRE-rTool, and multimodel direct inversion [MMDI] in clinical reconstruction) and acquiring dimensions (2D and 3D acquisitions). This study also tested the accuracy of masking low SNR regions using modified and conventional motion SNR under various mechanical vibration powers.

RESULTS

The stiffness values estimated using AIDE/LFE in MRE-rTool were comparable to that of MMDI (phantom, 3.71 ± 0.74, 3.60 ± 0.32, and 3.60 ± 0.54 kPa in AIDE, LFE, and MMDI; liver, 2.26 ± 0.31, 2.74 ± 0.16, and 2.21 ± 0.26 kPa in AIDE, LFE, and MMDI). The stiffness value in 3D acquisition was independent of the direction of the motion-encoding gradient and was more accurate than that of 2D acquisition. The masking of low SNR regions using the modified motion SNR worked better than that in the conventional motion SNR for each vibration power, especially when using a directional filter.

CONCLUSION

The performance of MRE-rTool on test data reached the level required in clinical MRE studies. MRE-rTool has the potential to facilitate MRE research, contribute to the future development of MRE, and has been freely released online.

In vivo characterization of brain tumor biomechanics: magnetic resonance elastography in intracranial B16 melanoma and GL261 glioma mouse models

Introduction

Magnetic Resonance Elastography (MRE) allows the non-invasive quantification of tumor biomechanical properties in vivo. With increasing incidence of brain metastases, there is a notable absence of appropriate preclinical models to investigate their biomechanical characteristics. Therefore, the purpose of this work was to assess the biomechanical characteristics of B16 melanoma brain metastases (MBM) and compare it to murine GL261 glioblastoma (GBM) model using multifrequency MRE with tomoelastography post processing.

Methods

Intracranial B16 MBM (n = 6) and GL261 GBM (n = 7) mouse models were used. Magnetic Resonance Imaging (MRI) was performed at set intervals after tumor implantation: 5, 7, 12, 14 days for MBM and 13 and 22 days for GBM. The investigations were performed using a 7T preclinical MRI with 20 mm head coil. The protocol consisted of single-shot spin echo-planar multifrequency MRE with tomoelastography post processing, contrast-enhanced T1- and T2-weighted imaging and diffusion-weighted imaging (DWI) with quantification of apparent diffusion coefficient of water (ADC). Elastography quantified shear wave speed (SWS), magnitude of complex MR signal (T2/T2*) and loss angle (φ). Immunohistological investigations were performed to assess vascularization, blood-brain-barrier integrity and extent of glucosaminoglucan coverage.

Results

Volumetric analyses displayed rapid growth of both tumor entities and softer tissue properties than healthy brain (healthy: 5.17 ± 0.48, MBM: 3.83 ± 0.55, GBM: 3.7 ± 0.23, [m/s]). SWS of MBM remained unchanged throughout tumor progression with decreased T2/T2* intensity and increased ADC on days 12 and 14 (p<0.0001 for both). Conversely, GBM presented reduced φ values on day 22 (p=0.0237), with no significant alterations in ADC. Histological analysis revealed substantial vascularization and elevated glycosaminoglycan content in both tumor types compared to healthy contralateral brain.

Discussion

Our results indicate that while both, MBM and GBM, exhibited softer properties compared to healthy brain, imaging and histological analysis revealed different underlying microstructural causes: hemorrhages in MBM and increased vascularization and glycosaminoglycan content in GBM, further corroborated by DWI and T2/T2* contrast. These findings underscore the complementary nature of MRE and its potential to enhance our understanding of tumor characteristics when used alongside established techniques. This comprehensive approach could lead to improved clinical outcomes and a deeper understanding of brain tumor pathophysiology.

Brain elastography in aging relates to fluid/solid trendlines

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

Wavelet MRE: Imaging propagating broadband acoustic waves with wavelet-based motion-encoding gradients

PURPOSE

To demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform.

METHODS

A wavelet MRE sequence was developed with motion-encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground-truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90-Hz motion) and steady-state dual-frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel.

RESULTS

In simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study.

CONCLUSION

The uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low-amplitude, transient vibrations as a model for the study of traumatic brain injury.

Magnetic resonance elastography resolving all gross anatomical segments of the kidney during controlled hydration

Introduction: Magnetic resonance elastography (MRE) is a non-invasive method to quantify biomechanical properties of human tissues. It has potential in diagnosis and monitoring of kidney disease, if established in clinical practice. The interplay of flow and volume changes in renal vessels, tubule, urinary collection system and interstitium is complex, but physiological ranges of in vivo viscoelastic properties during fasting and hydration have never been investigated in all gross anatomical segments simultaneously. Method: Ten healthy volunteers underwent two imaging sessions, one following a 12-hour fasting period and the second after a drinking challenge of >10 mL per kg body weight (60-75 min before the second examination). High-resolution renal MRE was performed using a novel driver with rotating eccentric mass placed at the posterior-lateral wall to couple waves (50 Hz) to the kidney. The biomechanical parameters, shear wave speed (cs in m/s), storage modulus (Gd in kPa), loss modulus (Gl in kPa), phase angle ( Υ = 2 π atan G l G d ) and attenuation (α in 1/mm) were derived. Accurate separation of gross anatomical segments was applied in post-processing (whole kidney, cortex, medulla, sinus, vessel). Results: High-quality shear waves coupled into all gross anatomical segments of the kidney (mean shear wave displacement: 163 ± 47 μm, mean contamination of second upper harmonics <23%, curl/divergence: 4.3 ± 0.8). Regardless of the hydration state, median Gd of the cortex and medulla (0.68 ± 0.11 kPa) was significantly higher than that of the sinus and vessels (0.48 ± 0.06 kPa), and consistently, significant differences were found in cs, Υ , and Gl (all p < 0.001). The viscoelastic parameters of cortex and medulla were not significantly different. After hydration sinus exhibited a small but significant reduction in median Gd by -0.02 ± 0.04 kPa (p = 0.01), and, consequently, the cortico-sinusoidal-difference in Gd increased by 0.04 ± 0.07 kPa (p = 0.05). Only upon hydration, the attenuation in vessels became lower (0.084 ± 0.013 1/mm) and differed significantly from the whole kidney (0.095 ± 0.007 1/mm, p = 0.01). Conclusion: High-resolution renal MRE with an innovative driver and well-defined 3D segmentation can resolve all renal segments, especially when including the sinus in the analysis. Even after a prolonged hydration period the approach is sensitive to small hydration-related changes in the sinus and in the cortico-sinusoidal-difference.

Changes in Tissue Fluidity Predict Tumor Aggressiveness In Vivo

Cancer progression is caused by genetic changes and associated with various alterations in cell properties, which also affect a tumor's mechanical state. While an increased stiffness has been well known for long for solid tumors, it has limited prognostic power. It is hypothesized that cancer progression is accompanied by tissue fluidization, where portions of the tissue can change position across different length scales. Supported by tabletop magnetic resonance elastography (MRE) on stroma mimicking collagen gels and microscopic analysis of live cells inside patient derived tumor explants, an overview is provided of how cancer associated mechanisms, including cellular unjamming, proliferation, microenvironment composition, and remodeling can alter a tissue's fluidity and stiffness. In vivo, state-of-the-art multifrequency MRE can distinguish tumors from their surrounding host tissue by their rheological fingerprints. Most importantly, a meta-analysis on the currently available clinical studies is conducted and universal trends are identified. The results and conclusions are condensed into a gedankenexperiment about how a tumor can grow and eventually metastasize into its environment from a physics perspective to deduce corresponding mechanical properties. Based on stiffness, fluidity, spatial heterogeneity, and texture of the tumor front a roadmap for a prognosis of a tumor's aggressiveness and metastatic potential is presented.

MR Elastography With Optimization-Based Phase Unwrapping and Traveling Wave Expansion-Based Neural Network (TWENN)

Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images. An objective function with Dual Data Consistency (Dual-DC) has been used to ensure accurate phase unwrapping and displacement extraction. For the estimation of complex wavenumbers, a complex-valued neural network with displacement covariance as an input has been developed. A model of traveling wave expansion is used to generate training datasets for the network with varying levels of noise. The complex shear modulus map is obtained through fusion of multifrequency and multidirectional data. Validation using brain and liver simulation images demonstrates the practical value of the proposed pipeline, which can estimate the biomechanical properties with minimal root-mean-square errors when compared to state-of-the-art methods. Applications of the proposed method for processing MRE images of phantom, brain, and liver reveal clear anatomical features, robustness to noise, and good generalizability of the pipeline.

Multi-frequency shear modulus measurements discriminate tumorous from healthy tissues

As far as their mechanical properties are concerned, cancerous lesions can be confused with healthy surrounding tissues in elastography protocols if only the magnitude of moduli is considered. We show that the frequency dependence of the tissue's mechanical properties allows for discriminating the tumor from other tissues, obtaining a good contrast even when healthy and tumor tissues have shear moduli of comparable magnitude. We measured the shear modulus G^*(ω) of xenograft subcutaneous tumors developed in mice using breast human cancer cells, compared with that of fat, skin and muscle harvested from the same mice. As the absolute shear modulus |G^*(ω)| of tumors increases by 42% (from 5.2 to 7.4 kPa) between 0.25 and 63 Hz, it varies over the same frequency range by 77% (from 0.53 to 0.94 kPa) for the fat, by 103% (from 3.4 to 6.9 kPa) for the skin and by 120% (from 4.4 to 9.7 kPa) for the muscle. These measurements fit well to the fractional model G^*(ω)=K(iω)ⁿ, yielding a coefficient K and a power-law exponent n for each sample. Tumor, skin and muscle have comparable K parameter values, that of fat being significantly lower; the p-values given by a Mann-Whitney test are above 0.14 when comparing tumor, skin and muscle between themselves, but below 0.001 when comparing fat with tumor, skin or muscle. With regards the n parameter, tumor and fat are comparable, with p-values above 0.43, whereas tumor differs from both skin and muscle, with p-values below 0.001. Tumor tissues thus significantly differs from fat, skin and muscle on account of either the K or the n parameter, i.e. of either the magnitude or the frequency-dependence of the shear modulus.

OSCILLATE: A low-rank approach for accelerated magnetic resonance elastography

PURPOSE

MR elastography (MRE) is a technique to characterize brain mechanical properties in vivo. Due to the need to capture tissue deformation in multiple directions over time, MRE is an inherently long acquisition, which limits achievable resolution and use in challenging populations. The purpose of this work is to develop a method for accelerating MRE acquisition by using low-rank image reconstruction to exploit inherent spatiotemporal correlations in MRE data.

THEORY AND METHODS

The proposed MRE sampling and reconstruction method, OSCILLATE (Observing Spatiotemporal Correlations for Imaging with Low-rank Leveraged Acceleration in Turbo Elastography), involves alternating which k-space points are sampled between each repetition by a reduction factor, ROSC. Using a predetermined temporal basis from a low-resolution navigator in a joint low-rank image reconstruction, all images can be accurately reconstructed from a reduced amount of k-space data.

RESULTS

Decomposition of MRE displacement data demonstrated that, on average, 96.1% of all energy from an MRE dataset is captured at rank L = 12 (reduced from a full rank of 24). Retrospectively undersampling data with ROSC  = 2 and reconstructing at low-rank (L = 12) yields highly accurate stiffness maps with voxel-wise error of 5.8% ± 0.7%. Prospectively undersampled data at ROSC  = 2 were successfully reconstructed without loss of material property map fidelity, with average global stiffness error of 1.0% ± 0.7% compared to fully sampled data.

CONCLUSIONS

OSCILLATE produces whole-brain MRE data at 2 mm isotropic resolution in 1 min 48 s.

Comparison of inversion methods in MR elastography: An open-access pipeline for processing multifrequency shear-wave data and demonstration in a phantom, human kidneys, and brain

PURPOSE

Magnetic resonance elastography (MRE) maps the viscoelastic properties of soft tissues for diagnostic purposes. However, different MRE inversion methods yield different results, which hinder comparison of values, standardization, and establishment of quantitative MRE markers. Here, we introduce an expandable, open-access, webserver-based platform that offers multiple inversion techniques for multifrequency, 3D MRE data.

METHODS

The platform comprises a data repository and standard MRE inversion methods including local frequency estimation (LFE), direct-inversion based multifrequency dual elasto-visco (MDEV) inversion, and wavenumber-based (k-) MDEV. The use of the platform is demonstrated in phantom data and in vivo multifrequency MRE data of the kidneys and brains of healthy volunteers.

RESULTS

Detailed maps of stiffness were generated by all inversion methods showing similar detail of anatomy. Specifically, the inner renal cortex had higher shear wave speed (SWS) than renal medulla and outer cortex without lateral differences. k-MDEV yielded higher SWS values than MDEV or LFE (full kidney/brain k-MDEV: 2.71 ± 0.19/1.45 ± 0.14 m/s, MDEV: 2.14 ± 0.16/0.99 ± 0.11 m/s, LFE: 2.12 ± 0.15/0.89 ± 0.06 m/s).

CONCLUSION

The freely accessible platform supports the comparison of MRE results obtained with different inversion methods, filter thresholds, or excitation frequencies, promoting reproducibility in MRE across community-developed methods.

Design, Construction, and Implementation of a Magnetic Resonance Elastography Actuator for Research Purposes

Magnetic resonance elastography (MRE) is a technique for determining the mechanical response of soft materials using applied harmonic deformation of the material and a motion-sensitive magnetic resonance imaging sequence. This technique can elucidate significant information about the health and development of human tissue such as liver and brain, and has been used on phantom models (e.g., agar, silicone) to determine their suitability for use as a mechanical surrogate for human tissues in experimental models. The applied harmonic deformation used in MRE is generated by an actuator, transmitted in bursts of a specified duration, and synchronized with the magnetic resonance signal excitation. These actuators are most often a pneumatic design (common for human tissues or phantoms) or a piezoelectric design (common for small animal tissues or phantoms). Here, we describe how to design and assemble both a pneumatic and a piezoelectric MRE actuator for research purposes. For each of these actuator types, we discuss displacement requirements, end-effector options and challenges, electronics and electronic-driving requirements and considerations, and full MRE implementation. We also discuss how to choose the actuator type, size, and power based on the intended material and use. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Design, construction, and implementation of a convertible pneumatic MRE actuator for use with tissues and phantom models Basic Protocol 2: Design, construction, and implementation of a piezoelectric MRE actuator for localized excitation in phantom models.

In vivo stiffness of multiple sclerosis lesions is similar to that of normal-appearing white matter

In 1868, French neurologist Jean-Martin Charcot coined the term multiple sclerosis (MS) after his observation that numerous white matter (WM) glial scars felt like sclerotic tissue. Nowadays, magnetic resonance elastography (MRE) can generate images with contrast of stiffness (CS) in soft in vivo tissues and may therefore be sensitive to MS lesions, provided that sclerosis is indeed a mechanical signature of this disease. We analyzed CS in a total of 147 lesions in patients with relapsing-remitting MS, compared with control regions in contralateral brain regions, and phantom data as well as performed numerical simulations to determine the delineation limits of multifrequency MRE (20 - 40 Hz) in MS. MRE analysis of simulated waves revealed a delineation limit of approximately 10% CS for detecting 9-mm lesions (mean size in our patient population). Due to inversion bias, this limit is reached when true CS is -11% for soft and 35% for stiff lesions. In vivo MRE identified 35 stiffer lesions and 17 softer lesions compared with surrounding WM (mean stiffness: 934±82 Pa). However, a similar pattern was found in the contralateral brain, suggesting that the range of stiffness changes in WM lesions due to MS is within the normal range of WM variability and normal heterogeneity-related CS. Consequently, Charcot's original intuition that MS is a focal sclerotic disease can neither be dismissed nor confirmed by in vivo MRE. However, the observation that MS lesions do not markedly differ in stiffness from surrounding brain tissue suggests that marked tissue sclerosis is not a mechanical signature of MS. STATEMENT OF SIGNIFICANCE: Multiple sclerosis (MS) was named by J.M. Charcot after the sclerotic changes in brain tissue he found in post-mortem autopsies. Since then, nothing has been revealed about the actual stiffening of MS lesions in vivo. Studying the viscoelastic properties of plaques in their natural environment is a major challenge that can only be overcome by MR elastography (MRE). Therefore, we used multifrequency MRE to answer the question whether MS lesions in patients with a relapsing-remitting disease course are mechanically different than surrounding tissue. Our findings suggest that the range of stiffness changes in white matter lesions due to MS is within the normal range of white matter variability and in vivo tissue sclerosis might not be a mechanical signature of MS.

Whole tissue and single cell mechanics are correlated in human brain tumors

Biomechanical changes are critical for cancer progression. However, the relationship between the rheology of single cells measured ex-vivo and the living tumor is not yet understood. Here, we combined single-cell rheology of cells isolated from primary tumors with in vivo bulk tumor rheology in patients with brain tumors. Eight brain tumors (3 glioblastoma, 3 meningioma, 1 astrocytoma, 1 metastasis) were investigated in vivo by magnetic resonance elastography (MRE), and after surgery by the optical stretcher (OS). MRE was performed in a 3-Tesla clinical MRI scanner and magnitude modulus |G*|, loss angle φ, storage modulus G', and loss modulus G'' were derived. OS experiments measured cellular creep deformation in response to laser-induced step stresses. We used a Kelvin-Voigt model to deduce two parameters related to cellular stiffness (μKV) and cellular viscosity (ηKV) from OS measurements in a time regimen that overlaps with that of MRE. We found that single-cell μKV was correlated with |G*| (R = 0.962, p < 0.001) and G'' (R = 0.883, p = 0.004) but not G' of the bulk tissue. These results suggest that single-cell stiffness affects tissue viscosity in brain tumors. The observation that viscosity parameters of individual cells and bulk tissue were not correlated suggests that collective mechanical interactions (i.e. emergent effects or cellular unjamming) of many cancer cells, which depend on cellular stiffness, influence the mechanical dissipation behavior of the bulk tissue. Our results are important to understand the emergent rheology of active multiscale compound materials such as brain tumors and its role in disease progression.

Microscopic multifrequency MR elastography for mapping viscoelasticity in zebrafish

PURPOSE

The zebrafish (Danio rerio) has become an important animal model in a wide range of biomedical research disciplines. Growing awareness of the role of biomechanical properties in tumor progression and neuronal development has led to an increasing interest in the noninvasive mapping of the viscoelastic properties of zebrafish by elastography methods applicable to bulky and nontranslucent tissues.

METHODS

Microscopic multifrequency MR elastography is introduced for mapping shear wave speed (SWS) and loss angle (φ) as markers of stiffness and viscosity of muscle, brain, and neuroblastoma tumors in postmortem zebrafish with 60 µm in-plane resolution. Experiments were performed in a 7 Tesla MR scanner at 1, 1.2, and 1.4 kHz driving frequencies.

RESULTS

Detailed zebrafish viscoelasticity maps revealed that the midbrain region (SWS = 3.1 ± 0.7 m/s, φ = 1.2 ± 0.3 radian [rad]) was stiffer and less viscous than telencephalon (SWS = 2.6 ± 0. 5 m/s, φ = 1.4 ± 0.2 rad) and optic tectum (SWS = 2.6 ± 0.5 m/s, φ = 1.3 ± 0.4 rad), whereas the cerebellum (SWS = 2.9 ± 0.6 m/s, φ = 0.9 ± 0.4 rad) was stiffer but less viscous than both (all p < .05). Overall, brain tissue (SWS = 2.9 ± 0.4 m/s, φ = 1.2 ± 0.2 rad) had similar stiffness but lower viscosity values than muscle tissue (SWS = 2.9 ± 0.5 m/s, φ = 1.4 ± 0.2 rad), whereas neuroblastoma (SWS = 2.4 ± 0.3 m/s, φ = 0.7 ± 0.1 rad, all p < .05) was the softest and least viscous tissue.

CONCLUSION

Microscopic multifrequency MR elastography-generated maps of zebrafish show many details of viscoelasticity and resolve tissue regions, of great interest in neuromechanical and oncological research and for which our study provides first reference values.

Microscale characterisation of the time-dependent mechanical behaviour of brain white matter

Brain mechanics is a topic of deep interest because of the significant role of mechanical cues in both brain function and form. Specifically, capturing the heterogeneous and anisotropic behaviour of cerebral white matter (WM) is extremely challenging and yet the data on WM at a spatial resolution relevant to tissue components are sparse. To investigate the time-dependent mechanical behaviour of WM, and its dependence on local microstructural features when subjected to small deformations, we conducted atomic force microscopy (AFM) stress relaxation experiments on corpus callosum (CC), corona radiata (CR) and fornix (FO) of fresh ovine brain. Our experimental results show a dependency of the tissue mechanical response on axons orientation, with e.g. the stiffness of perpendicular and parallel samples is different in all three regions of WM whereas the relaxation behaviour is different for the CC and FO regions. An inverse modelling approach was adopted to extract Prony series parameters of the tissue components, i.e. axons and extra cellular matrix with its accessory cells, from experimental data. Using a bottom-up approach, we developed analytical and FEA estimates that are in good agreement with our experimental results. Our systematic characterisation of sheep brain WM using a combination of AFM experiments and micromechanical models provide a significant contribution for predicting localised time-dependent mechanics of brain tissue. This information can lead to more accurate computational simulations, therefore aiding the development of surgical robotic solutions for drug delivery and accurate tissue mimics, as well as the determination of criteria for tissue injury and predict brain development and disease progression.

Practical and clinical applications of pancreatic magnetic resonance elastography: a systematic review

Magnetic resonance elastography (MRE) is a non-invasive technique suitable for assessing mechanical properties of tissues, i.e., stiffness. MRE of the pancreas is relatively new, but recently an increasing number of studies have successfully assessed pancreas diseases with MRE aiming to differentiate healthy from pathological pancreatic tissue with or without fibrosis. This review will systematically describe the practical and clinical applications of pancreatic MRE. We conducted a systematic literature search with a pre-specified search strategy using PubMed and Embase according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. English peer-reviewed articles applying MRE of the pancreas were included. Two independent reviewers assessed the studies. The literature search yielded 14 studies. The pancreatic stiffness for healthy volunteers ranged from 1.11. to 1.21 kPa at a driver frequency of 40 Hz. In benign tumors, the stiffness values were slightly higher or sometimes even lower (range 0.78 to 2.00 kPa), compared to the healthy pancreas parenchyma whereas, in malignant tumors, the stiffness values tended to be higher (1.42 to 6.06 kPa). The pancreatic stiffness was increased in both acute (median: 1.99 kPa) and chronic pancreatitis (> 1.50 kPa). MRE is a promising technique for detecting and quantifying pancreatic stiffness. It is related to fibrosis and seems to be useful in assessing treatment response and clinical follow-up of pancreatic diseases. However, most of the described practical settings were characterized by a lack of uniformity and inconsistency in reporting standards across studies. Harmonization between centers is necessary to achieve more consensus and optimization of pancreatic MRE protocols.

Viscoelasticity Imaging of Biological Tissues and Single Cells Using Shear Wave Propagation

Changes in biomechanical properties of biological soft tissues are often associated with physiological dysfunctions. Since biological soft tissues are hydrated, viscoelasticity is likely suitable to represent its solid-like behavior using elasticity and fluid-like behavior using viscosity. Shear wave elastography is a non-invasive imaging technology invented for clinical applications that has shown promise to characterize various tissue viscoelasticity. It is based on measuring and analyzing velocities and attenuations of propagated shear waves. In this review, principles and technical developments of shear wave elastography for viscoelasticity characterization from organ to cellular levels are presented, and different imaging modalities used to track shear wave propagation are described. At a macroscopic scale, techniques for inducing shear waves using an external mechanical vibration, an acoustic radiation pressure or a Lorentz force are reviewed along with imaging approaches proposed to track shear wave propagation, namely ultrasound, magnetic resonance, optical, and photoacoustic means. Then, approaches for theoretical modeling and tracking of shear waves are detailed. Following it, some examples of applications to characterize the viscoelasticity of various organs are given. At a microscopic scale, a novel cellular shear wave elastography method using an external vibration and optical microscopy is illustrated. Finally, current limitations and future directions in shear wave elastography are presented.

Aging brain mechanics: Progress and promise of magnetic resonance elastography

Neuroimaging techniques that can sensitivity characterize healthy brain aging and detect subtle neuropathologies have enormous potential to assist in the early detection of neurodegenerative conditions such as Alzheimer's disease. Magnetic resonance elastography (MRE) has recently emerged as a reliable, high-resolution, and especially sensitive technique that can noninvasively characterize tissue biomechanical properties (i.e., viscoelasticity) in vivo in the living human brain. Brain tissue viscoelasticity provides a unique biophysical signature of neuroanatomy that are representative of the composition and organization of the complex tissue microstructure. In this article, we detail how progress in brain MRE technology has provided unique insights into healthy brain aging, neurodegeneration, and structure-function relationships. We further discuss additional promising technical innovations that will enhance the specificity and sensitivity for brain MRE to reveal considerably more about brain aging as well as its potentially valuable role as an imaging biomarker of neurodegeneration. MRE sensitivity may be particularly useful for assessing the efficacy of rehabilitation strategies, assisting in differentiating between dementia subtypes, and in understanding the causal mechanisms of disease which may lead to eventual pharmacotherapeutic development.

Real-Time Multifrequency MR Elastography of the Human Brain Reveals Rapid Changes in Viscoelasticity in Response to the Valsalva Maneuver

Modulation of cerebral blood flow and vascular compliance plays an important role in the regulation of intracranial pressure (ICP) and also influences the viscoelastic properties of brain tissue. Therefore, magnetic resonance elastography (MRE), the gold standard for measuring in vivo viscoelasticity of brain tissue, is potentially sensitive to cerebral autoregulation. In this study, we developed a multifrequency MMRE technique that provides serial maps of viscoelasticity at a frame rate of nearly 6 Hz without gating, i.e., in quasi-real time (rt-MMRE). This novel method was used to monitor rapid changes in the viscoelastic properties of the brains of 17 volunteers performing the Valsalva maneuver (VM). rt-MMRE continuously sampled externally induced vibrations comprising three frequencies of 30.03, 30.91, and 31.8 Hz were over 90 s using a steady-state, spiral-readout gradient-echo sequence. Data were processed by multifrequency dual elasto-visco (MDEV) inversion to generate maps of magnitude shear modulus | G^∗| (stiffness) and loss angle φ at a frame rate of 5.4 Hz. As controls, the volunteers were examined to study the effects of breath-hold following deep inspiration and breath-hold following expiration. We observed that | G^∗| increased while φ decreased due to VM and, less markedly, due to breath-hold in inspiration. Group mean VM values showed an early overshoot of | G^∗| 2.4 ± 1.2 s after the onset of the maneuver with peak values of 6.7 ± 4.1% above baseline, followed by a continuous increase in stiffness during VM. A second overshoot of | G^∗| occurred 5.5 ± 2.0 s after the end of VM with peak values of 7.4 ± 2.8% above baseline, followed by 25-s sustained recovery until the end of image acquisition. φ was constantly reduced by approximately 2% during the entire VM without noticeable peak values. This is the first report of viscoelasticity changes in brain tissue induced by physiological maneuvers known to alter ICP and detected by clinically applicable rt-MMRE. Our results show that apnea and VM slightly alter brain properties toward a more rigid-solid behavior. Overshooting stiffening reactions seconds after onset and end of VM reveal rapid autoregulatory processes of brain tissue viscoelasticity.

MR Elastography of the Abdomen: Basic Concepts

Magnetic resonance elastography (MRE) is an emerging imaging modality that maps the elastic properties of tissue such as the shear modulus. It allows for noninvasive assessment of stiffness, which is a surrogate for fibrosis. MRE has been shown to accurately distinguish absent or low stage fibrosis from high stage fibrosis, primarily in the liver. Like other elasticity imaging modalities, it follows the general steps of elastography: (1) apply a known cyclic mechanical vibration to the tissue; (2) measure the internal tissue displacements caused by the mechanical wave using magnetic resonance phase encoding method; and (3) infer the mechanical properties from the measured mechanical response (displacement), by generating a simplified displacement map. The generated map is called an elastogram.While the key interest of MRE has traditionally been in its application to liver, where in humans it is FDA approved and commercially available for clinical use to noninvasively assess degree of fibrosis, this is an area of active research and there are novel upcoming applications in brain, kidney, pancreas, spleen, heart, lungs, and so on. A detailed review of all the efforts is beyond the scope of this chapter, but a few specific examples are provided. Recent application of MRE for noninvasive evaluation of renal fibrosis has great potential for noninvasive assessment in patients with chronic kidney diseases. Development and applications of MRE in preclinical models is necessary primarily to validate the measurement against "gold-standard" invasive methods, to better understand physiology and pathophysiology, and to evaluate novel interventions. Application of MRE acquisitions in preclinical settings involves challenges in terms of available hardware, logistics, and data acquisition. This chapter will introduce the concepts of MRE and provide some illustrative applications.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by another separate chapter describing the experimental protocol and data analysis.

SNR Weighting for Shear Wave Speed Reconstruction in Tomoelastography

In tomoelastography, to achieve a final wave speed map by combining reconstructions obtained from all spatial directions and excitation frequencies, the use of weights is inevitable. Here, a new weighting scheme, which maximizes the signal-to-noise ratio (SNR) of the final wave speed map, has been proposed. To maximize the SNR of the final wave speed map, the use of squares of estimated SNR values of reconstructed individual maps has been proposed. Therefore, derivations of the SNR of the reconstructed wave speed maps have become necessary. Considering the noise on the complex MRI signal, the SNR of the reconstructed wave speed map was formulated by an analytical approach assuming a high SNR, and the results were verified using Monte Carlo simulations (MCSs). It has been assumed that the noise remains approximately Gaussian when the image SNR is high enough, despite the nonlinear operations in tomoelastography inversion. Hence, the SNR threshold was determined by comparing the SNR computed by MCSs and analytical approximations. The weighting scheme was evaluated for accuracy, spatial resolution and SNR performances on simulated phantoms. MR elastography (MRE) experiments on two different phantoms were conducted. Wave speed maps were generated for simulated 3D human abdomen MRE data and experimental human abdomen MRE data. The simulation results demonstrated that the SNR-weighted inversion improved the SNR performance of the wave speed map by a factor of two compared to the performance of the original (i.e., amplitude-weighted) reconstruction. In the case of a low SNR, no bias occurred in the wave speed map when SNR weighting was used, whereas 10% bias occurred when the original weighting (i.e., amplitude weighting) was used. Thus, while not altering the accuracy or spatial resolution of the wave speed map with the proposed weighting method, the SNR of the wave speed map has been significantly improved.

MR elastography: Principles, guidelines, and terminology

Magnetic resonance elastography (MRE) is a phase contrast-based MRI technique that can measure displacement due to propagating mechanical waves, from which material properties such as shear modulus can be calculated. Magnetic resonance elastography can be thought of as quantitative, noninvasive palpation. It is increasing in clinical importance, has become widespread in the diagnosis and staging of liver fibrosis, and additional clinical applications are being explored. However, publications have reported MRE results using many different parameters, acquisition techniques, processing methods, and varied nomenclature. The diversity of terminology can lead to confusion (particularly among clinicians) about the meaning of and interpretation of MRE results. This paper was written by the MRE Guidelines Committee, a group formalized at the first meeting of the ISMRM MRE Study Group, to clarify and move toward standardization of MRE nomenclature. The purpose of this paper is to (1) explain MRE terminology and concepts to those not familiar with them, (2) define "good practices" for practitioners of MRE, and (3) identify opportunities to standardize terminology, to avoid confusion.

Enhanced complex local frequency elastography method for tumor viscoelastic shear modulus reconstruction

BACKGROUND AND OBJECTIVES

The Mayo Clinic provides a magnetic resonance (MR) elastography software named MRE Wave, which uses the conventional local frequency elastography (LFE) method. However, MRE Wave is unable to supply complex viscoelasticity maps for elastography. We sought to improve the local frequency estimation algorithm used in LFE, which we refer to as the Enhanced Complex Local Frequency Elastography (EC-LFE) algorithm.

METHODS

The proposed algorithm uses wave equations under the hypotheses of being linear, isotropic, and locally homogeneous. Two 2D simulation models were used to investigate the accuracy and sensitivity of the EC-LFE algorithm for detecting small tumors. The corresponding statistical parameters were the relative root mean square (RMS) error and contrast-to-noise ratio (CNR). EC-LFE was investigated with two different parameter sets, one with an optimally chosen parameter ξ (EC-LFE Adj, for short) and the other with ξ = 0 (EC-LFE0). We compared the MRE Wave and the EC-LFE using series signal-to-noise (SNR) wave data.

RESULTS

The elasticity RMS error of the MRE Wave software was about 1%, and that of the EC-LFE0 and EC-LFE Adj were about 0.2%. The elasticity standard deviation of the MRE Wave software was about 3% of the mean value, and those of the EC-LFE0 and EC-LFE Adj were about 1% of the mean value. The elasticity CNR value of EC-LFE0 reached 1.93 times that of the MRE Wave in the region of small tumors (less than 10-point sampling). The viscosity RMS errors of the EC-LFE0 could be less than 5%.

CONCLUSION

Compared to conventional methods, the EC-LFE was more accurate and sensitive for small tumor detection and exhibited higher noise immunity. The improved algorithm output more parameters and outperformed than the MRE Wave, thereby rendering them more suitable for clinical applications.

Gradient Based Elastic Property Reconstruction in Digital Image Correlation Elastography

Aim

A Conjugate Gradient implementation of the Digital Image Correlation Elastography method is presented.

Method

The gradient is calculated using the adjoint method, requiring only two forward solutions regardless of the number of mechanical properties to reconstruct. A power-law based multi-frequency viscoelastic model is used to relate the reconstructed mechanical properties and the Digital Image Correlation surface displacement measurements.

Result

The method is tested against harmonic surface motion fields generated through numerical simulation and measured on a silicon phantom.

Conclusion

Reconstruction results show the ability of the gradient reconstruction method to detect stiff internal inclusions in simulated and phantom data.

Towards microstructure-informed material models for human brain tissue

Emerging evidence suggests that the mechanical behavior of the brain plays a critical role in development, disease, and aging. Recent studies have begun to characterize the mechanical behavior of gray and white matter tissue and to identify sets of material models that best reproduce the stress-strain behavior of different brain regions. Yet, these models are mainly phenomenological in nature, their parameters often lack clear physical interpretation, and they fail to correlate the mechanical behavior to the underlying microstructural composition. Here we make a first attempt towards identifying general relations between microstructure and mechanics with the ultimate goal to develop microstructurally motivated constitutive equations for human brain tissue. Using histological staining, we analyze the microstructure of brain specimens from different anatomical regions, the cortex, basal ganglia, corona radiata, and corpus callosum, and identify the regional stiffness and viscosity under multiple loading conditions, simple shear, compression, and tension. Strikingly, our study reveals a negative correlation between cell count and stiffness, a positive correlation between myelin content and stiffness, and a negative correlation between proteoglycan content and stiffness. Additionally, our analysis shows a positive correlation between lipid and proteoglycan content and viscosity. We demonstrate how understanding the microstructural origin of the macroscopic behavior of the brain can help us design microstructure-informed material models for human brain tissue that inherently capture regional heterogeneities. This study represents an important step towards using brain tissue stiffness and viscosity as early diagnostic markers for clinical conditions including chronic traumatic encephalopathy, Alzheimer's and Parkinson's disease, or multiple sclerosis. STATEMENT OF SIGNIFICANCE: The complex and heterogeneous mechanical properties of brain tissue play a critical role for brain function. To understand and predict how brain tissue properties vary in space and time, it will be key to link the mechanical behavior to the underlying microstructural composition. Here we use histological staining to quantify area fractions of microstructural components of mechanically tested specimens and evaluate their individual contributions to the nonlinear macroscopic mechanical response. We further propose a microstructure-informed material model for human brain tissue that inherently captures regional heterogeneities. The current work provides unprecedented insights into the biomechanics of human brain tissue, which are highly relevant to develop refined computational models for brain tissue behavior or to advance neural tissue engineering.

How tissue fluidity influences brain tumor progression

Mechanical properties of biological tissues and, above all, their solid or fluid behavior influence the spread of malignant tumors. While it is known that solid tumors tend to have higher mechanical rigidity, allowing them to aggressively invade and spread in solid surrounding healthy tissue, it is unknown how softer tumors can grow within a more rigid environment such as the brain. Here, we use in vivo magnetic resonance elastography (MRE) to elucidate the role of anomalous fluidity for the invasive growth of soft brain tumors, showing that aggressive glioblastomas (GBMs) have higher water content while behaving like solids. Conversely, our data show that benign meningiomas (MENs), which contain less water than brain tissue, are characterized by fluid-like behavior. The fact that the 2 tumor entities do not differ in their soft properties suggests that fluidity plays an important role for a tumor's aggressiveness and infiltrative potential. Using tissue-mimicking phantoms, we show that the anomalous fluidity of neurotumors physically enables GBMs to penetrate surrounding tissue, a phenomenon similar to Saffman-Taylor viscous-fingering instabilities, which occur at moving interfaces between fluids of different viscosity. Thus, targeting tissue fluidity of malignant tumors might open horizons for the diagnosis and treatment of cancer.

Optimization of a Multifrequency Magnetic Resonance Elastography Protocol for the Human Brain

BACKGROUND AND PURPOSE

The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM).

METHODS

Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC).

RESULTS

BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB.

CONCLUSIONS

Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.

Magnetic Resonance Elastography of Liver: Current Update

The first clinical application of magnetic resonance elastography (MRE) was in the evaluation of chronic liver disease (CLD) for detection and staging of liver fibrosis. In the past 10 years, MRE has been incorporated seamlessly into a standard magnetic resonance imaging (MRI) liver protocol worldwide. Liver MRE is a robust technique for evaluation of liver stiffness and is currently the most accurate noninvasive imaging technology for evaluation of liver fibrosis. Newer MRE sequences including spin-echo MRE and 3 dimensional MRE have helped in reducing the technical limitations of clinical liver MRE that is performed with 2D gradient recalled echo (GRE) MRE. Advances in MRE technology have led to understanding of newer mechanical parameters such as dispersion, attenuation, and viscoelasticity that may be useful in evaluating pathological processes in CLD and may prove useful in their management.This review article will describe the changes in CLD that cause an increase in stiffness followed by principle and technique of liver MRE. In the later part of the review, we will briefly discuss the advances in liver MRE.

Magnetic resonance elastography of liver and spleen: Methods and applications

The viscoelastic properties of the liver and spleen can be assessed with magnetic resonance elastography (MRE). Several actuators, MRI acquisition sequences and reconstruction algorithms have been proposed for this purpose. Reproducible results are obtained, especially when the examination is performed in standard conditions with the patient fasting. Accurate staging of liver fibrosis can be obtained by measuring liver stiffness or elasticity with MRE. Moreover, emerging evidence shows that assessing the tissue viscous parameters with MRE is useful for characterizing liver inflammation, non-alcoholic steatohepatitis, hepatic congestion, portal hypertension, and hepatic tumors. Further advances such as multifrequency acquisitions and compression-sensitive MRE may provide novel quantitative markers of hepatic and splenic mechanical properties that may improve the diagnosis of hepatic and splenic diseases.

Stiffness and Beyond: What MR Elastography Can Tell Us About Brain Structure and Function Under Physiologic and Pathologic Conditions

Brain magnetic resonance elastography (MRE) was developed on the basis of a desire to "palpate by imaging" and is becoming a powerful tool in the investigation of neurophysiological and neuropathological states. Measurements are acquired with a specialized MR phase-contrast pulse sequence that can detect tissue motion in response to an applied external or internal excitation. The tissue viscoelasticity is then reconstructed from the measured displacement. Quantitative characterization of brain viscoelastic behaviors provides us an insight into the brain structure and function by assessing the mechanical rigidity, viscosity, friction, and connectivity of brain tissues. Changes in these features are associated with inflammation, demyelination, and neurodegeneration that contribute to brain disease onset and progression. Here, we review the basic principles and limitations of brain MRE and summarize its current neuroanatomical studies and clinical applications to the most common neurosurgical and neurodegenerative disorders, including intracranial tumors, dementia, multiple sclerosis, amyotrophic lateral sclerosis, and traumatic brain injury. Going forward, further improvement in acquisition techniques, stable inverse reconstruction algorithms, and advanced numerical, physical, and preclinical validation models is needed to increase the utility of brain MRE in both research and clinical applications.

Stiffness reconstruction methods for MR elastography

Assessment of tissue stiffness is desirable for clinicians and researchers, as it is well established that pathophysiological mechanisms often alter the structural properties of tissue. Magnetic resonance elastography (MRE) provides an avenue for measuring tissue stiffness and has a long history of clinical application, including staging liver fibrosis and stratifying breast cancer malignancy. A vital component of MRE consists of the reconstruction algorithms used to derive stiffness from wave-motion images by solving inverse problems. A large range of reconstruction methods have been presented in the literature, with differing computational expense, required user input, underlying physical assumptions, and techniques for numerical evaluation. These differences, in turn, have led to varying accuracy, robustness, and ease of use. While most reconstruction techniques have been validated against in silico or in vitro phantoms, performance with real data is often more challenging, stressing the robustness and assumptions of these algorithms. This article reviews many current MRE reconstruction methods and discusses the aforementioned differences. The material assumptions underlying the methods are developed and various approaches for noise reduction, regularization, and numerical discretization are discussed. Reconstruction methods are categorized by inversion type, underlying assumptions, and their use in human and animal studies. Future directions, such as alternative material assumptions, are also discussed.

Development of a robust diffusion-MR elastography (dMRE) technique to mitigate intravoxel phase dispersion

Diffusion-magnetic resonance elastography (dMRE) is an emerging practical technique that can acquire diffusion magnetic resonance imaging and MRE simultaneously. However, a signal loss attributable to intravoxel phase dispersion (IVPD) interferes with the calculation of the apparent diffusion coefficient (ADC). This study presents an approach to dMRE that reduces the influence of IVPD by introducing a new pulse sequence. The existing and proposed techniques were performed using a phantom comprising five rods with different elasticities at 60 Hz vibration to investigate the accuracy of previous and proposed dMRE techniques. The measures of ADC and stiffness, obtained by using both dMRE techniques, were compared with conventional spin-echo (SE) diffusion and SE-MRE. Then, we evaluated those differences by using the mean of absolute differences (MAD) in each rod within the phantom. The results of the MAD of the stiffness from both dMRE techniques showed almost no difference. In contrast, the value of the ADC MAD (MAD ≒ 0.16 × 10^-3 mm²/s), obtained in the soft region within the phantom with the previous dMRE technique, was large. This value was about 2.7 times that of the value produced by the proposed dMRE technique. This difference must reflect the degree of influence of IVPD in both techniques. These results demonstrate that our dMRE technique is a robust method for addressing the signal loss attributable to IVPD.

Quantitative Elastography Methods in Liver Disease: Current Evidence and Future Directions

Chronic liver diseases often result in the development of liver fibrosis and ultimately, cirrhosis. Treatment strategies and prognosis differ greatly depending on the severity of liver fibrosis, thus liver fibrosis staging is clinically relevant. Traditionally, liver biopsy has been the method of choice for fibrosis evaluation. Because of liver biopsy limitations, noninvasive methods have become a key research interest in the field. Elastography enables the noninvasive measurement of tissue mechanical properties through observation of shear-wave propagation in the tissue of interest. Increasing fibrosis stage is associated with increased liver stiffness, providing a discriminatory feature that can be exploited by elastographic methods. Ultrasonographic (US) and magnetic resonance (MR) imaging elastographic methods are commercially available, each with their respective strengths and limitations. Here, the authors review the technical basis, acquisition techniques, and results and limitations of US- and MR-based elastography techniques. Diagnostic performance in the most common etiologies of chronic liver disease will be presented. Reliability, reproducibility, failure rate, and emerging advances will be discussed. ^© RSNA, 2018 Online supplemental material is available for this article.

Magnetic Resonance Elastography to Assess Fibrosis in Kidney Allografts

BACKGROUND AND OBJECTIVES

Fibrosis is a major cause of kidney allograft injury. Currently, the only means of assessing allograft fibrosis is by biopsy, an invasive procedure that samples <1% of the kidney. We examined whether magnetic resonance elastography, an imaging-based measure of organ stiffness, could noninvasively estimate allograft fibrosis and predict progression of allograft dysfunction.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Kidney allograft recipients >1 year post-transplant undergoing an allograft biopsy first underwent free-breathing, flow-compensated magnetic resonance elastography on a 3.0-T magnetic resonance imaging scanner. Each patient had serial eGFR measurements after the elastography scan for a follow-up period of up to 1 year. The mean stiffness value of the kidney allograft was compared with both the histopathologic Banff fibrosis score and the rate of eGFR change during the follow-up period.

RESULTS

Sixteen patients who underwent magnetic resonance elastography and biopsy were studied (mean age: 54±9 years old). Whole-kidney mean stiffness ranged between 3.5 and 7.3 kPa. Whole-kidney stiffness correlated with biopsy-derived Banff fibrosis score (Spearman rho =0.67; P<0.01). Stiffness was heterogeneously distributed within each kidney, providing a possible explanation for the lack of a stronger stiffness-fibrosis correlation. We also found negative correlations between whole-kidney stiffness and both baseline eGFR (Spearman rho =-0.65; P<0.01) and eGFR change over time (Spearman rho =-0.70; P<0.01). Irrespective of the baseline eGFR, increased kidney stiffness was associated with a greater eGFR decline (regression r2=0.48; P=0.03).

CONCLUSIONS

Given the limitations of allograft biopsy, our pilot study suggests the potential for magnetic resonance elastography as a novel noninvasive measure of whole-allograft fibrosis burden that may predict future changes in kidney function. Future studies exploring the utility and accuracy of magnetic resonance elastography are needed.

Finite element analysis to investigate variability of MR elastography in the human thigh

PURPOSE

To develop finite element analysis (FEA) of magnetic resonance elastography (MRE) in the human thigh and investigate inter-individual variability of measurement of muscle mechanical properties.

METHODS

Segmentation was performed on MRI datasets of the human thigh from 5 individuals and FEA models consisting of 12 muscles and surrounding tissue created. The same material properties were applied to each tissue type and a previously developed transient FEA method of simulating MRE using Abaqus was performed at 4 frequencies. Synthetic noise was applied to the simulated data at various levels before inversion was performed using the Elastography Software Pipeline. Maps of material properties were created and visually assessed to determine key features. The coefficient of variation (CoV) was used to assess the variability of measurements in each individual muscle and in the groups of muscles across the subjects. Mean measurements for the set of muscles were ranked in size order and compared with the expected ranking.

RESULTS

At noise levels of 2% the CoV in measurements of |G^*| ranged from 5.3 to 21.9% and from 7.1 to 36.1% for measurements of ϕ in the individual muscles. A positive correlation (R² value 0.80) was attained when the expected and measured |G^*| ranking were compared, whilst a negative correlation (R² value 0.43) was found for ϕ.

CONCLUSIONS

Created elastograms demonstrated good definition of muscle structure and were robust to noise. Variability of measurements across the 5 subjects was dramatically lower for |G^*| than it was for ϕ. This large variability in ϕ measurements was attributed to artefacts.

Mechanical characterization of human brain tissue

Mechanics are increasingly recognized to play an important role in modulating brain form and function. Computational simulations are a powerful tool to predict the mechanical behavior of the human brain in health and disease. The success of these simulations depends critically on the underlying constitutive model and on the reliable identification of its material parameters. Thus, there is an urgent need to thoroughly characterize the mechanical behavior of brain tissue and to identify mathematical models that capture the tissue response under arbitrary loading conditions. However, most constitutive models have only been calibrated for a single loading mode. Here, we perform a sequence of multiple loading modes on the same human brain specimen - simple shear in two orthogonal directions, compression, and tension - and characterize the loading-mode specific regional and directional behavior. We complement these three individual tests by combined multiaxial compression/tension-shear tests and discuss effects of conditioning and hysteresis. To explore to which extent the macrostructural response is a result of the underlying microstructural architecture, we supplement our biomechanical tests with diffusion tensor imaging and histology. We show that the heterogeneous microstructure leads to a regional but not directional dependence of the mechanical properties. Our experiments confirm that human brain tissue is nonlinear and viscoelastic, with a pronounced compression-tension asymmetry. Using our measurements, we compare the performance of five common constitutive models, neo-Hookean, Mooney-Rivlin, Demiray, Gent, and Ogden, and show that only the isotropic modified one-term Ogden model is capable of representing the hyperelastic behavior under combined shear, compression, and tension loadings: with a shear modulus of 0.4-1.4kPa and a negative nonlinearity parameter it captures the compression-tension asymmetry and the increase in shear stress under superimposed compression but not tension. Our results demonstrate that material parameters identified for a single loading mode fail to predict the response under arbitrary loading conditions. Our systematic characterization of human brain tissue will lead to more accurate computational simulations, which will allow us to determine criteria for injury, to develop smart protection systems, and to predict brain development and disease progression.

STATEMENT OF SIGNIFICANCE

There is a pressing need to characterize the mechanical behavior of human brain tissue under multiple loading conditions, and to identify constitutive models that are able to capture the tissue response under these conditions. We perform a sequence of experimental tests on the same brain specimen to characterize the regional and directional behavior, and we supplement our tests with DTI and histology to explore to which extent the macrostructural response is a result of the underlying microstructure. Results demonstrate that human brain tissue is nonlinear and viscoelastic, with a pronounced compression-tension asymmetry, and we show that the multiaxial data can best be captured by a modified version of the one-term Ogden model.

Nonlinear multiscale regularisation in MR elastography: Towards fine feature mapping

Fine-featured elastograms may provide additional information of radiological interest in the context of in vivo elastography. Here a new image processing pipeline called ESP (Elastography Software Pipeline) is developed to create Magnetic Resonance Elastography (MRE) maps of viscoelastic parameters (complex modulus magnitude |G^*| and loss angle ϕ) that preserve fine-scale information through nonlinear, multi-scale extensions of typical MRE post-processing techniques.

METHODS

A new MRE image processing pipeline was developed that incorporates wavelet-domain denoising, image-driven noise estimation, and feature detection. ESP was first validated using simulated data, including viscoelastic Finite Element Method (FEM) simulations, at multiple noise levels. ESP images were compared with MDEV pipeline images, both in the FEM models and in three ten-subject cohorts of brain, thigh, and liver acquisitions. ESP and MDEV mean values were compared to 2D local frequency estimation (LFE) mean values for the same cohorts as a benchmark. Finally, the proportion of spectral energy at fine frequencies was quantified using the Reduced Energy Ratio (RER) for both ESP and MDEV.

RESULTS

Blind estimates of added noise (σ) were within 5.3% ± 2.6% of prescribed, and the same technique estimated σ in the in vivo cohorts at 1.7 ± 0.8%. A 5 × 5 × 5 truncated Gabor filter bank effectively detects local spatial frequencies at wavelengths λ ≤ 10px. For FEM inversions, mean |G^*| of hard target, soft target, and background remained within 8% of prescribed up to σ=20%, and mean ϕ results were within 10%, excepting hard target ϕ, which required redrawing around a ring artefact to achieve similar accuracy. Inspection of FEM |G^*| images showed some spatial distortion around hard target boundaries and inspection of ϕ images showed ring artefacts around the same target. For the in vivo cohorts, ESP results showed mean correlation of R=0.83 with MDEV and liver stiffness estimates within 7% of 2D-LFE results. Finally, ESP showed statistically significant increase in fine feature spectral energy as measured with RER for both |G^*| (p<1×10^-9) and ϕ (p<1×10^-3).

CONCLUSION

Information at finer frequencies can be recovered in ESP elastograms in typical experimental conditions, however scatter- and boundary-related artefacts may cause the fine features to have inaccurate values. In in vivo cohorts, ESP delivers an increase in fine feature spectral energy, and better performance with longer wavelengths, than MDEV while showing similar stability and robustness.