High-resolution mechanical imaging of the human brain by three-dimensional multifrequency magnetic resonance elastography at 7T

Characterizing brain mechanics through 7 tesla magnetic resonance elastography

Magnetic resonance elastography (MRE) is a non-invasive method for determining the mechanical response of tissues using applied harmonic deformation and motion-sensitive MRI. MRE studies of the human brain are typically performed at conventional field strengths, with a few attempts at the ultra-high field strength, 7T, reporting increased spatial resolution with partial brain coverage. Achieving high-resolution human brain scans using 7T MRE presents unique challenges of decreased octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and lower shear wave motion sensitivity. In this study, we establish high resolution MRE at 7T with a custom 2D multi-slice single-shot spin-echo echo-planar imaging sequence, using the Gadgetron advanced image reconstruction framework, applying Marchenko-Pastur Principal component analysis denoising, and using nonlinear viscoelastic inversion. These techniques allowed us to calculate the viscoelastic properties of the whole human brain at 1.1 mm isotropic imaging resolution with high OSS-SNR and repeatability. Using phantom models and 7T MRE data of eighteen healthy volunteers, we demonstrate the robustness and accuracy of our method at high-resolution while quantifying the feasible tradeoff between resolution, OSS-SNR, and scan time. Using these post-processing techniques, we significantly increased OSS-SNR at 1.1 mm resolution with whole-brain coverage by approximately 4-fold and generated elastograms with high anatomical detail. Performing high-resolution MRE at 7T on the human brain can provide information on different substructures within brain tissue based on their mechanical properties, which can then be used to diagnose pathologies (e.g. Alzheimer's disease), indicate disease progression, or better investigate neurodegeneration effects or other relevant brain disorders,in vivo.

Estimating the viscoelastic properties of the human brain at 7 T MRI using intrinsic MRE and nonlinear inversion

Intrinsic actuation magnetic resonance elastography (MRE) is a phase-contrast MRI technique that allows for in vivo quantification of mechanical properties of the brain by exploiting brain motion that arise naturally due to the cardiac pulse. The mechanical properties of the brain reflect its tissue microstructure, making it a potentially valuable parameter in studying brain disease. The main purpose of this study was to assess the feasibility of reconstructing the viscoelastic properties of the brain using high-quality 7 T MRI displacement measurements, obtained using displacement encoding with stimulated echoes (DENSE) and intrinsic actuation. The repeatability and sensitivity of the method for detecting normal regional variation in brain tissue properties was assessed as secondary goal. The displacement measurements used in this analysis were previously acquired for a separate study, where eight healthy subjects (27 ± 7 years) were imaged with repeated scans (spatial resolution approx. 2 mm isotropic, temporal resolution 75 ms, motion sensitivity 0.35 mm/2π for displacements in anterior-posterior and left-right directions, and 0.7 mm/2π for feet-head displacements). The viscoelastic properties of the brain were estimated using a subzone based non-linear inversion scheme. The results show comparable consistency to that of extrinsic MRE between the viscoelastic property maps obtained from repeated displacement measurements. The shear stiffness maps showed fairly consistent spatial patterns. The whole-brain repeatability coefficient (RC) for shear stiffness was (mean ± standard deviation) 8 ± 8% relative to the mean whole-brain stiffness, and the damping ratio RC was 28 ± 17% relative to the whole-brain damping ratio. The shear stiffness maps showed similar statistically significant regional trends as demonstrated in a publicly available atlas of viscoelastic properties obtained with extrinsic actuation MRE at 50 Hz. The damping ratio maps showed less consistency, likely due to data-model mismatch of describing the brain as a viscoelastic material under low frequencies. While artifacts induced by fluid flow within the brain remain a limitation of the technique in its current state, intrinsic actuation based MRE allow for consistent and repeatable estimation of the mechanical properties of the brain. The method provides enough sensitivity to investigate regional variation in such properties in the normal brain, which is likely sufficient to also investigate pathological changes.

An engineered glioblastoma model yields novel macrophage-secreted drivers of invasion

Glioblastomas (GBMs) are highly invasive brain tumors replete with brain- and blood-derived macrophages, collectively known as tumor-associated macrophages (TAMs). Targeting TAMs has been proposed as a therapeutic strategy but has thus far yielded limited clinical success in slowing GBM progression, due in part to an incomplete understanding of TAM function in GBM. Here, by using an engineered hyaluronic acid-based 3D invasion platform, patient-derived GBM cells, and multi-omics analysis of GBM tumor microenvironments, we show that M2-polarized macrophages stimulate GBM stem cell (GSC) mesenchymal transition and invasion. We identify TAM-derived transforming growth factor beta induced (TGFβI/BIGH3) as a pro-tumorigenic factor in the GBM microenvironment. In GBM patients, BIGH3 mRNA expression correlates with poor patient prognosis and is highest in the most aggressive GBM molecular subtype. Inhibiting TAM-derived BIGH3 signaling with a blocking antibody or small molecule inhibitor suppresses GSC invasion. Our work highlights the utility of 3D in vitro tumor microenvironment platforms to investigate TAM-cancer cell crosstalk and offers new insights into TAM function to guide novel TAM-targeting therapies.

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.

In vivo corneal elastography: A topical review of challenges and opportunities

Clinical measurement of corneal biomechanics can aid in the early diagnosis, progression tracking, and treatment evaluation of ocular diseases. Over the past two decades, interdisciplinary collaborations between investigators in optical engineering, analytical biomechanical modeling, and clinical research has expanded our knowledge of corneal biomechanics. These advances have led to innovations in testing methods (ex vivo, and recently, in vivo) across multiple spatial and strain scales. However, in vivo measurement of corneal biomechanics remains a long-standing challenge and is currently an active area of research. Here, we review the existing and emerging approaches for in vivo corneal biomechanics evaluation, which include corneal applanation methods, such as ocular response analyzer (ORA) and corneal visualization Scheimpflug technology (Corvis ST), Brillouin microscopy, and elastography methods, and the emerging field of optical coherence elastography (OCE). We describe the fundamental concepts, analytical methods, and current clinical status for each of these methods. Finally, we discuss open questions for the current state of in vivo biomechanics assessment techniques and requirements for wider use that will further broaden our understanding of corneal biomechanics for the detection and management of ocular diseases, and improve the safety and efficacy of future clinical practice.

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.

Magnetic resonance elastography with guided pressure waves

Magnetic resonance elastography aims to non-invasively and remotely characterize the mechanical properties of living tissues. To quantitatively and regionally map the shear viscoelastic moduli in vivo, the technique must achieve proper mechanical excitation throughout the targeted tissues. Although it is straightforward, ante manibus, in close organs such as the liver or the breast, which practitioners clinically palpate already, it is somewhat fortunately highly challenging to trick the natural protective barriers of remote organs such as the brain. So far, mechanical waves have been induced in the latter by shaking the surrounding cranial bones. Here, the skull was circumvented by guiding pressure waves inside the subject's buccal cavity so mechanical waves could propagate from within through the brainstem up to the brain. Repeatable, reproducible and robust displacement fields were recorded in phantoms and in vivo by magnetic resonance elastography with guided pressure waves such that quantitative mechanical outcomes were extracted in the human brain.

Evaluation of cerebral cortex viscoelastic property estimation with nonlinear inversion magnetic resonance elastography

Objective. Magnetic resonance elastography (MRE) of the brain has shown promise as a sensitive neuroimaging biomarker for neurodegenerative disorders; however, the accuracy of performing MRE of the cerebral cortex warrants investigation due to the unique challenges of studying thinner and more complex geometries.Approach. A series of realistic, whole-brain simulation experiments are performed to examine the accuracy of MRE to measure the viscoelasticity (shear stiffness,μ, and damping ratio, ξ) of cortical structures predominantly effected in aging and neurodegeneration. Variations to MRE spatial resolution and the regularization of a nonlinear inversion (NLI) approach are examined.Main results. Higher-resolution MRE displacement data (1.25 mm isotropic resolution) and NLI with a low soft prior regularization weighting provided minimal measurement error compared to other studied protocols. With the optimized protocol, an average error inμand ξ was 3% and 11%, respectively, when compared with the known ground truth. Mid-line structures, as opposed to those on the cortical surface, generally display greater error. Varying model boundary conditions and reducing the thickness of the cortex by up to 0.67 mm (which is a realistic portrayal of neurodegenerative pathology) results in no loss in reconstruction accuracy.Significance. These experiments establish quantitative guidelines for the accuracy expected ofin vivoMRE of the cortex, with the proposed method providing valid MRE measures for future investigations into cortical viscoelasticity and relationships with health, cognition, and behavior.

Sexual Dimorphism in Extracellular Matrix Composition and Viscoelasticity of the Healthy and Inflamed Mouse Brain

Simple Summary

In multiple sclerosis (MS), an autoimmune disease of the central nervous system that primarily affects women, gender differences in disease course and in brain softening have been reported. It has been shown that the molecular network found between the cells of the tissue, the extracellular matrix (ECM), influences tissue stiffness. However, it is still unclear if sex influences ECM composition. Therefore, here we investigated how brain ECM and stiffness differ between sexes in the healthy mouse, and in an MS mouse model. We applied multifrequency magnetic resonance elastography and gene expression analysis for associating in vivo brain stiffness with ECM protein content in the brain, such as collagen and laminin. We found that the cortex was softer in males than in females in both healthy and sick mice. Softening was associated with sex differences in expression levels of collagen and laminin. Our findings underscore the importance of considering sex when studying the constitution of brain tissue in health and disease, particularly when investigating the processes underlying gender differences in MS.

Abstract

Magnetic resonance elastography (MRE) has revealed sexual dimorphism in brain stiffness in healthy individuals and multiple sclerosis (MS) patients. In an animal model of MS, named experimental autoimmune encephalomyelitis (EAE), we have previously shown that inflammation-induced brain softening was associated with alterations of the extracellular matrix (ECM). However, it remained unclear whether the brain ECM presents sex-specific properties that can be visualized by MRE. Therefore, here we aimed at quantifying sexual dimorphism in brain viscoelasticity in association with ECM changes in healthy and inflamed brains. Multifrequency MRE was applied to the midbrain of healthy and EAE mice of both sexes to quantitatively map regional stiffness. To define differences in brain ECM composition, the gene expression of the key basement membrane components laminin (Lama4, Lama5), collagen (Col4a1, Col1a1), and fibronectin (Fn1) were investigated by RT-qPCR. We showed that the healthy male cortex expressed less Lama4, Lama5, and Col4a1, but more Fn1 (all p < 0.05) than the healthy female cortex, which was associated with 9% softer properties (p = 0.044) in that region. At peak EAE cortical softening was similar in both sexes compared to healthy tissue, with an 8% difference remaining between males and females (p = 0.006). Cortical Lama4, Lama5 and Col4a1 expression increased 2 to 3-fold in EAE in both sexes while Fn1 decreased only in males (all p < 0.05). No significant sex differences in stiffness were detected in other brain regions. In conclusion, sexual dimorphism in the ECM composition of cortical tissue in the mouse brain is reflected by in vivo stiffness measured with MRE and should be considered in future studies by sex-specific reference values.

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.

An electromagnetic actuator for brain magnetic resonance elastography with high frequency accuracy

Our goal is to design, test and verify an electromagnetic actuator for brain magnetic resonance elastography (MRE). We proposed a grappler-shaped design that can transmit stable vibrations into the brain. To validate its performance, simulations were carried out to ensure the electromagnetic field generated by the actuator did not interfere with the B₀ field. The actuation vibration spectrum was analyzed to verify the actuation accuracy. Phantom and volunteer experiments were carried out to evaluate the performance of the actuator. Simulation of the magnetic field showed that the proposed actuator has a fringe field of less than 3 G in the imaging region. The phantom experiments showed that the proposed actuator did not interfere with the routine imaging sequences. The measured vibration spectra demonstrated that the frequency offset was about one third that of a pneumatic device and the transmission efficiency was three times higher. The shear moduli estimated from brain MRE were consistent with those from the literature. The actuation frequency of the proposed actuator has less frequency offset and off-center frequency components compared with the pneumatic counterpart. The whole actuator weighted only 980 g. The actuator can carry out multifrequency MRE on the brain with high accuracy. It is easy to use, comfortable for the patient and portable.

Fast 3D MR elastography of the whole brain using spiral staircase: Data acquisition, image reconstruction, and joint deblurring

PURPOSE

To address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods.

METHODS

We combined a novel 3D spiral staircase data-acquisition method with a spoiled gradient-echo pulse sequence and MRE motion-encoding gradients (MEGs). The spiral-out acquisition permitted use of longer-duration motion-encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase-SNR. Through-plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion.

RESULTS

In vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin-echo-based approaches, both qualitatively and quantitatively. High-resolution (2-mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR-enhanced images, leading to upward stiffness estimation.

CONCLUSION

A novel 3D gradient-echo-based approach has been designed and implemented, and shown to have promising potential for fast and high-resolution in vivo MRE of the whole brain.

Brain-mimicking phantom for biomechanical validation of motion sensitive MR imaging techniques

Motion sensitive MR imaging techniques allow for the non-invasive evaluation of biological tissues by using different excitation schemes, including physiological/intrinsic motions caused by cardiac pulsation or respiration, and vibrations caused by an external actuator. The mechanical biomarkers extracted through these imaging techniques have been shown to hold diagnostic value for various neurological disorders and conditions. Amplified MRI (aMRI), a cardiac gated imaging technique, can help track and quantify low frequency intrinsic motion of the brain. As for high frequency actuation, the mechanical response of brain tissue can be measured by applying external high frequency actuation in combination with a motion sensitive MR imaging sequence called Magnetic Resonance Elastography (MRE). Due to the frequency-dependent behavior of brain mechanics, there is a need to develop brain phantom models that can mimic the broadband mechanical response of the brain in order to validate motion-sensitive MR imaging techniques. Here, we have designed a novel phantom test setup that enables both the low and high frequency responses of a brain-mimicking phantom to be captured, allowing for both aMRI and MRE imaging techniques to be applied on the same phantom model. This setup combines two different vibration sources: a pneumatic actuator, for low frequency/intrinsic motion (1 Hz) for use in aMRI, and a piezoelectric actuator for high frequency actuation (30-60 Hz) for use in MRE. Our results show that in MRE experiments performed from 30 Hz through 60 Hz, propagating shear waves attenuate faster at higher driving frequencies, consistent with results in the literature. Furthermore, actuator coupling has a substantial effect on wave amplitude, with weaker coupling causing lower amplitude wave field images, specifically shown in the top-surface shear loading configuration. For intrinsic actuation, our results indicate that aMRI linearly amplifies motion up to at least an amplification factor of 9 for instances of both visible and sub-voxel motion, validated by varying power levels of pneumatic actuation (40%-80% power) under MR, and through video analysis outside the MRI scanner room. While this investigation used a homogeneous brain-mimicking phantom, our setup can be used to study the mechanics of non-homogeneous phantom configurations with bio-interfaces in the future.

Inversion-recovery MR elastography of the human brain for improved stiffness quantification near fluid-solid boundaries

PURPOSE

In vivo MR elastography (MRE) holds promise as a neuroimaging marker. In cerebral MRE, shear waves are introduced into the brain, which also stimulate vibrations in adjacent CSF, resulting in blurring and biased stiffness values near brain surfaces. We here propose inversion-recovery MRE (IR-MRE) to suppress CSF signal and improve stiffness quantification in brain surface areas.

METHODS

Inversion-recovery MRE was demonstrated in agar-based phantoms with solid-fluid interfaces and 11 healthy volunteers using 31.25-Hz harmonic vibrations. It was performed by standard single-shot, spin-echo EPI MRE following 2800-ms IR preparation. Wave fields were acquired in 10 axial slices and analyzed for shear wave speed (SWS) as a surrogate marker of tissue stiffness by wavenumber-based multicomponent inversion.

RESULTS

Phantom SWS values near fluid interfaces were 7.5 ± 3.0% higher in IR-MRE than MRE (P = .01). In the brain, IR-MRE SNR was 17% lower than in MRE, without influencing parenchymal SWS (MRE: 1.38 ± 0.02 m/s; IR-MRE: 1.39 ± 0.03 m/s; P = .18). The IR-MRE tissue-CSF interfaces appeared sharper, showing 10% higher SWS near brain surfaces (MRE: 1.01 ± 0.03 m/s; IR-MRE: 1.11 ± 0.01 m/s; P < .001) and 39% smaller ventricle sizes than MRE (P < .001).

CONCLUSIONS

Our results show that brain MRE is affected by fluid oscillations that can be suppressed by IR-MRE, which improves the depiction of anatomy in stiffness maps and the quantification of stiffness values in brain surface areas. Moreover, we measured similar stiffness values in brain parenchyma with and without fluid suppression, which indicates that shear wavelengths in solid and fluid compartments are identical, consistent with the theory of biphasic poroelastic media.

Ultrasound Shear Wave Elastography and Transient Optical Coherence Elastography: Side-by-Side Comparison of Repeatability and Accuracy

Objective: We compare the repeatability and accuracy of ultrasound shear wave elastography (USE) and transient optical coherence elastography (OCE). Methods: Elastic wave speed in gelatin phantoms and chicken breast was measured with USE and OCE and compared with uniaxial mechanical compression testing. Intra- and Inter-repeatability were analyzed using Bland-Altman plots and intraclass correlation coefficients (ICC). Results: OCE and USE differed from uniaxial testing by a mean absolute percent error of 8.92% and 16.9%, respectively, across eight phantoms of varying stiffness. Upper and lower limits of agreement for intrasample repeatability for USE and OCE were ±0.075 m/s and −0.14 m/s and 0.13 m/s, respectively. OCE and USE both had ICCs of 0.9991. In chicken breast, ICC for USE was 0.9385 and for OCE was 0.9924. Conclusion: OCE and USE can detect small speed changes and give comparable measurements. These measurements correspond well with uniaxial testing.

Harnessing brain waves: a review of brain magnetic resonance elastography for clinicians and scientists entering the field

Brain magnetic resonance elastography (MRE) is an imaging technique capable of accurately and non-invasively measuring the mechanical properties of the living human brain. Recent studies have shown that MRE has potential to provide clinically useful information in patients with intracranial tumors, demyelinating disease, neurodegenerative disease, elevated intracranial pressure, and altered functional states. The objectives of this review are: (1) to give a general overview of the types of measurements that have been obtained with brain MRE in patient populations, (2) to survey the tools currently being used to make these measurements possible, and (3) to highlight brain MRE-based quantitative biomarkers that have the highest potential of being adopted into clinical use within the next 5 to 10 years. The specifics of MRE methodology strategies are described, from wave generation to material parameter estimations. The potential clinical role of MRE for characterizing and planning surgical resection of intracranial tumors and assessing diffuse changes in brain stiffness resulting from diffuse neurological diseases and altered intracranial pressure are described. In addition, the emerging technique of functional MRE, the role of artificial intelligence in MRE, and promising applications of MRE in general neuroscience research are presented.

MR Elastography demonstrates reduced white matter shear stiffness in early-onset hydrocephalus

INTRODUCTION

Hydrocephalus that develops early in life is often accompanied by developmental delays, headaches and other neurological deficits, which may be associated with changes in brain shear stiffness. However, noninvasive approaches to measuring stiffness are limited. Magnetic Resonance Elastography (MRE) of the brain is a relatively new noninvasive imaging method that provides quantitative measures of brain tissue stiffness. Herein, we aimed to use MRE to assess brain stiffness in hydrocephalus patients compared to healthy controls, and to assess its associations with ventricular size, as well as demographic, shunt-related and clinical outcome measures.

METHODS

MRE was collected at two imaging sites in 39 hydrocephalus patients and 33 healthy controls, along with demographic, shunt-related, and clinical outcome measures including headache and quality of life indices. Brain stiffness was quantified for whole brain, global white matter (WM), and lobar WM stiffness. Group differences in brain stiffness between patients and controls were compared using two-sample t-tests and multivariable linear regression to adjust for age, sex, and ventricular volume. Among patients, multivariable linear or logistic regression was used to assess which factors (age, sex, ventricular volume, age at first shunt, number of shunt revisions) were associated with brain stiffness and whether brain stiffness predicts clinical outcomes (quality of life, headache and depression).

RESULTS

Brain stiffness was significantly reduced in patients compared to controls, both unadjusted (p ≤ 0.002) and adjusted (p ≤ 0.03) for covariates. Among hydrocephalic patients, lower stiffness was associated with older age in temporal and parietal WM and whole brain (WB) (beta (SE): -7.6 (2.5), p = 0.004; -9.5 (2.2), p = 0.0002; -3.7 (1.8), p = 0.046), being female in global and frontal WM and WB (beta (SE): -75.6 (25.5), p = 0.01; -66.0 (32.4), p = 0.05; -73.2 (25.3), p = 0.01), larger ventricular volume in global, and occipital WM (beta (SE): -11.5 (3.4), p = 0.002; -18.9 (5.4), p = 0.0014). Lower brain stiffness also predicted worse quality of life and a higher likelihood of depression, controlling for all other factors.

CONCLUSIONS

Brain stiffness is reduced in hydrocephalus patients compared to healthy controls, and is associated with clinically-relevant functional outcome measures. MRE may emerge as a clinically-relevant biomarker to assess the neuropathological effects of hydrocephalus and shunting, and may be useful in evaluating the effects of therapeutic alternatives, or as a supplement, of shunting.

Mechanosensation in traumatic brain injury

Traumatic brain injury (TBI) is distinct from other neurological disorders because it is induced by a discrete event that applies extreme mechanical forces to the brain. This review describes how the brain senses, integrates, and responds to forces under both normal conditions and during injury. The response to forces is influenced by the unique mechanical properties of brain tissue, which differ by region, cell type, and sub-cellular structure. Elements such as the extracellular matrix, plasma membrane, transmembrane receptors, and cytoskeleton influence its properties. These same components also act as force-sensors, allowing neurons and glia to respond to their physical environment and maintain homeostasis. However, when applied forces become too large, as in TBI, these components may respond in an aberrant manner or structurally fail, resulting in unique pathological sequelae. This so-called "pathological mechanosensation" represents a spectrum of cellular responses, which vary depending on the overall biomechanical parameters of the injury and may be compounded by repetitive injuries. Such aberrant physical responses and/or damage to cells along with the resulting secondary injury cascades can ultimately lead to long-term cellular dysfunction and degeneration, often resulting in persistent deficits. Indeed, pathological mechanosensation not only directly initiates secondary injury cascades, but this post-physical damage environment provides the context in which these cascades unfold. Collectively, these points underscore the need to use experimental models that accurately replicate the biomechanics of TBI in humans. Understanding cellular responses in context with injury biomechanics may uncover therapeutic targets addressing various facets of trauma-specific sequelae.

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.

Standard-space atlas of the viscoelastic properties of the human brain

Standard anatomical atlases are common in neuroimaging because they facilitate data analyses and comparisons across subjects and studies. The purpose of this study was to develop a standardized human brain atlas based on the physical mechanical properties (i.e., tissue viscoelasticity) of brain tissue using magnetic resonance elastography (MRE). MRE is a phase contrast‐based MRI method that quantifies tissue viscoelasticity noninvasively and in vivo thus providing a macroscopic representation of the microstructural constituents of soft biological tissue. The development of standardized brain MRE atlases are therefore beneficial for comparing neural tissue integrity across populations. Data from a large number of healthy, young adults from multiple studies collected using common MRE acquisition and analysis protocols were assembled (N = 134; 78F/ 56 M; 18–35 years). Nonlinear image registration methods were applied to normalize viscoelastic property maps (shear stiffness, μ, and damping ratio, ξ) to the MNI152 standard structural template within the spatial coordinates of the ICBM‐152. We find that average MRE brain templates contain emerging and symmetrized anatomical detail. Leveraging the substantial amount of data assembled, we illustrate that subcortical gray matter structures, white matter tracts, and regions of the cerebral cortex exhibit differing mechanical characteristics. Moreover, we report sex differences in viscoelasticity for specific neuroanatomical structures, which has implications for understanding patterns of individual differences in health and disease. These atlases provide reference values for clinical investigations as well as novel biophysical signatures of neuroanatomy. The templates are made openly available (github.com/mechneurolab/mre134) to foster collaboration across research institutions and to support robust cross‐center comparisons.

Quantitative analysis of vibration waves based on Fourier transform in magnetic resonance elastography

We developed a novel magnetic resonance elastography (MRE) analysis method based on Fourier transform to assess the responsive characteristics for different tissue stiffness and degree of transmission of the vibration wave emanating from a passive driver during MRE. A phantom tissue study was conducted with an MRE sequence and vibration wave system using a clinical MR scanner. The phantom tissue consisted of two layers of agar: 0.75 wt% and 1.0 wt%. Phase-unwrapped images derived from acquired MRE phase images were used to generate a phase profile curve, with a line plotted for the phase-unwrapped images. Fourier transform was performed, and the peak value of the power spectrum was derived. The damping rate/ratio was calculated using the Hilbert transform of the phase profile. We found that the mean shear stiffness value of 1.0 wt% agar was higher than that of 0.75 wt% agar. The responsive frequency of the 0.75 wt% agar layer showed a wider range and the damping rate of the signal showed a higher value than the respective values of the 1.0 wt% agar layer. In conclusion, Fourier transform analysis of MRE enabled us to obtain more detailed information of the tissue characteristics and vibration-wave conditions.

Requirements for accurate estimation of shear modulus by magnetic resonance elastography: A computational comparative study

BACKGROUND

Magnetic resonance (MR) elastography is a non-destructive method of measuring biological tissue and is conducive to the early detection of tumors. Researchers usually set different assumptions according to different research objects, then establish and solve wave equations to estimate the shear modulus. Establishing a more reasonable model for a measured object estimates a more accurate shear modulus. Different assumptions of the mathematical model, and the method used to solve the wave equation causes deviation of the estimation.

OBJECTIVE

This study focused on shear modulus deviations caused by differences in calculation methods. The author demonstrated a method to ensure that the measuring range of the selected reconstruction algorithm with selected drive frequency covers the elasticity range of the target tissue. It is hoped to arouse the interest of researchers to introduce new transform domain methods to the field of MR elastography.

METHOD

In linear, isotropic and local homogeneity assumptions, the typical representative of two different calculation methods are algebraic inversion of the differential equation (AIDE) algorithm and local frequency elastography (LFE) algorithm. To compare the accuracy of these calculation methods, the author adopted a digital phantom that can set the parameter values accurately. It is assumed that the phantom tissue was linear and isotropic, and that the driving wave was sinusoidal. The displacement distribution of waves in the tissue was calculated by the finite element simulation method in two different resolutions with the signal-to-noise ratio (SNR) set to 40 dB and the threshold of relative mean error (RME) no more than 10%. The wavelength-to-pixel-size ratios of the two methods under the setting threshold of RME were compared.

RESULTS

The lower threshold of wavelength-to-pixel-size ratio for AIDE was close to 10, while that for LFE was nearly 2 (the limitation of Shannon's law) under the setting precision. Thus, the measuring range of the AIDE method was less than that of LFE at the same experimental conditions.

CONCLUSION

The driving frequency selection range of the spatial frequency domain method is wider than that of the spatial domain method. It is worthwhile for researchers to devote more time to introducing new transformation domain method for MR elastography.

Hypercapnia increases brain viscoelasticity

Brain function, the brain's metabolic activity, cerebral blood flow (CBF), and intracranial pressure are intimately linked within the tightly autoregulated regime of intracranial physiology in which the role of tissue viscoelasticity remains elusive. We applied multifrequency magnetic resonance elastography (MRE) paired with CBF measurements in 14 healthy subjects exposed to 5-min carbon dioxide-enriched breathing air to induce cerebral vasodilatation by hypercapnia. Stiffness and viscosity as quantified by the magnitude and phase angle of the complex shear modulus, |G*| and ϕ, as well as CBF of the whole brain and 25 gray matter sub-regions were analyzed prior to, during, and after hypercapnia. In all subjects, whole-brain stiffness and viscosity increased due to hypercapnia by 3.3 ± 1.9% and 2.0 ± 1.1% which was accompanied by a CBF increase of 36 ± 15%. Post-hypercapnia, |G*| and ϕ reduced to normal values while CBF decreased by 13 ± 15% below baseline. Hypercapnia-induced viscosity changes correlated with CBF changes, whereas stiffness changes did not. The MRE-measured viscosity changes correlated with blood viscosity changes predicted by the Fåhræus-Lindqvist model and microvessel diameter changes from the literature. Our results suggest that brain viscoelastic properties are influenced by microvessel blood flow and blood viscosity: vasodilatation and increased blood viscosity due to hypercapnia result in an increase in MRE values related to viscosity.

Brain maturation is associated with increasing tissue stiffness and decreasing tissue fluidity

Biomechanical cues guide proliferation, growth and maturation of neurons. Yet the molecules that shape the brain's biomechanical properties are unidentified and the relationship between neural development and viscoelasticity of brain tissue remains elusive. Here we combined novel in-vivo tomoelastography and ex-vivo proteomics to investigate whether viscoelasticity of the mouse brain correlates with protein alterations within the critical phase of brain maturation. For the first time, high-resolution atlases of viscoelasticity of the mouse brain were generated, revealing that (i) brain stiffness increased alongside progressive accumulation of microtubular structures, myelination, cytoskeleton linkage and cell-matrix attachment, and that (ii) viscosity-related tissue fluidity decreased alongside downregulated actin crosslinking and axonal organization. Taken together, our results show that brain maturation is associated with a shift of brain mechanical properties towards a more solid-rigid behavior consistent with reduced tissue fluidity. This shift appears to be driven by several molecular processes associated with myelination, cytoskeletal crosslinking and axonal organization. STATEMENT OF SIGNIFICANCE: The viscoelastic properties of brain tissue shape the environment in which neurons proliferate, grow, and mature. In the present study, novel tomoelastography was used to spatially map tissue mechanical properties of the in-vivo mouse brain during maturation. In vivo tomoelastography was also combined with ex vivo mass spectrometry proteomic analysis to identify the molecules which shape the biomechanical properties of brain tissue. With the combined technique, we observed that brain maturation is associated with a shift of brain mechanical properties towards a more solid-rigid behavior consistent with reduced tissue fluidity which is driven by multiple molecular processes. We believe that this shift of brain mechanical properties discovered in our study reflects a fundamental biophysical signature of brain maturation.

Engineering approaches for characterizing soft tissue mechanical properties: A review

From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and functionality. Alterations in the structural features of tissues are often manifested as local stiffening and are early signs for diagnosing a disease. These elastic properties are measured ex vivo in conventional mechanical testing regimes, however, the heterogeneous microstructure of tissues can be accurately resolved over relatively smaller length scales with enhanced spatial resolution using techniques such as micro-indentation, microelectromechanical (MEMS) based cantilever sensors and optical catheters which also facilitate in vivo assessment of mechanical properties. In this review, we describe several probing strategies (qualitative and quantitative) based on the spatial scale of mechanical assessment and also discuss the potential use of machine learning techniques to compute the mechanical properties of soft tissues. This work details state of the art advancement in probing strategies, associated challenges toward quantitative characterization of tissue biomechanics both from an engineering and clinical standpoint.

Viscoelasticity of striatal brain areas reflects variations in body mass index of lean to overweight male adults

Although a variety of MRI studies investigated the link between body mass index (BMI) and parameters of neural gray matter (GM), the technique applied in most of these studies, voxel-based morphometry (VBM), focusses on the regional GM volume, a macroscopic tissue property. Thus, the studies were not able to exploit the BMI-related information contained in the GM microstructure although PET studies suggest that these factors are important. Here, we used cerebral MR Elastography (MRE) to characterize features of tissue microstructure by evaluating the propagation of shear waves applied to the skull and to assess local tissue viscoelasticity to test the link between this parameter and BMI in 22 lean to overweight males. Unlike the majority of existing MRE studies investigating neural viscoelasticity signals averaged across large brain regions, we used the viscoelasticity of individual voxels for our experiment. Our technique revealed a negative link between BMI and viscoelasticity of two areas of the striatal reward system, i.e., right putamen (t = -8.2; p_{FWE-corrected} = 0.005) and left globus pallidus (t = -7.1; p_FWE = 0.037) which was independent of GM volume at these coordinates. Finally, comparison of BMI models based on individual voxels vs. on signals averaged across brain atlas regions demonstrates that voxel-based models explain a significantly higher proportion of variance. Consequently, our findings show that cerebral MRE is suitable to identify medically relevant microstructural tissue properties. Using a voxel-wise analysis approach, we were able to utilize the high spatial resolution of MRE for mapping BMI-related information in the brain.

Reliable preparation of agarose phantoms for use in quantitative magnetic resonance elastography

Agarose phantoms are one type of phantom commonly used in developing in vivo brain magnetic resonance elastography (MRE) sequences because they are inexpensive and easy to work with, store, and dispose of; however, protocols for creating agarose phantoms are non-standardized and often result in inconsistent phantoms with significant variability in mechanical properties. Many magnetic resonance imaging (MRI) and ultrasound studies use phantoms, but often these phantoms are not tailored for desired mechanical properties and as such are too stiff or not mechanically consistent enough to be used in MRE. In this work, we conducted a systematic study of agarose phantom creation parameters to identify those factors that are most conducive to producing mechanically consistent agarose phantoms for MRE research. We found that cooling rate and liquid temperature affected phantom homogeneity. Phantom stiffness is affected by agar concentration (quadratically), by final liquid temperature and salt content in phantoms, and by the interaction of these two metrics each with stir rate. We captured and quantified the implied relationships with a regression model that can be used to estimate stiffness of resulting phantoms. Additionally, we characterized repeatability, stability over time, impact on MR signal parameters, and differences in agar gel microstructure. This protocol and regression model should prove beneficial in future MRE development studies that use phantoms to determine stiffness measurement accuracy.

MR elastography at 1 Hz of gelatin phantoms using 3D or 4D acquisition

Magnetic Resonance Elastography (MRE) detects induced periodic motions in biological tissues allowing maps of tissue mechanical properties to be derived. In-vivo MRE is commonly performed at frequencies of 30-100 Hz using external actuation, however, using cerebro-vascular pulsation at 1 Hz as a form of intrinsic actuation (IA-MRE) eliminates the need for external motion sources and simplifies data acquisition. In this study a hydraulic actuation system was developed to drive 1 Hz motions in gelatin as a tool for investigating the performance limits of IA-MRE image reconstruction under controlled conditions. Quantitative flow (QFLOW) MR techniques were used to phase encode 1 Hz motions as a function of gradient direction using 3D or 4D acquisition; 4D acquisition was twice as fast and yielded comparable motion field and concomitant image reconstruction results provided the motion signal was sufficiently strong. Per voxel motion noise floor corresponded to a displacement amplitude of about 20-30 μm. Signal to noise ratio (SNR) was 94 ± 17 for 3D and dropped to 69 ± 10 for the faster 4D acquisition, but yielded octahedral shear stress and shear modulus maps of high quality that differed by only about 20% on average. QFLOW measurements in gel phantoms were improved significantly by adding Mn(II) to mimic relaxation rates found in brain. Overall, the hydraulic 1 Hz actuation system when coupled with 4D sequence acquisition produced a fast reliable approach for future IA-MRE phantom evaluation and contrast detail studies needed to benchmark imaging performance.

Encoding and readout strategies in magnetic resonance elastography

Magnetic resonance elastography (MRE) has evolved significantly since its inception. Advances in motion-encoding gradient design and readout strategies have led to improved encoding and signal-to-noise ratio (SNR) efficiencies, which in turn allow for higher spatial resolution, increased coverage, and/or shorter scan times. The purpose of this review is to summarize MRE wave-encoding and readout approaches in a unified mathematical framework to allow for a comparative assessment of encoding and SNR efficiency of the various methods available. Besides standard full- and fractional-wave-encoding approaches, advanced techniques including flow compensation, sample interval modulation and multi-shot encoding are considered. Signal readout using fast k-space trajectories, reduced field of view, multi-slice, and undersampling techniques are summarized and put into perspective. The review is concluded with a foray into displacement and diffusion encoding as alternative and/or complementary techniques.

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.

Perspective: The role of mechanobiology in the etiology of brain metastasis

Tumor latency and dormancy are obstacles to effective cancer treatment. In brain metastases, emergence of a lesion can occur at varying intervals from diagnosis and in some cases following successful treatment of the primary tumor. Genetic factors that drive brain metastases have been identified, such as those involved in cell adhesion, signaling, extravasation, and metabolism. From this wealth of knowledge, vexing questions still remain; why is there a difference in strategy to facilitate outgrowth and why is there a difference in latency? One missing link may be the role of tissue biophysics of the brain microenvironment in infiltrating cells. Here, I discuss the mechanical cues that may influence disseminated tumor cells in the brain, as a function of age and disease. I further discuss in vitro and in vivo preclinical models such as 3D culture systems and zebrafish to study the role of the mechanical environment in brain metastasis in an effort of providing novel targeted therapeutics.

Double dissociation of structure-function relationships in memory and fluid intelligence observed with magnetic resonance elastography

Brain tissue mechanical properties, measured in vivo with magnetic resonance elastography (MRE), have proven to be sensitive metrics of neural tissue integrity. Recently, our group has reported on the positive relationship between viscoelasticity of the hippocampus and performance on a relational memory task in healthy young adults, which highlighted the potential of sensitive MRE measures for studying brain health and its relation to cognitive function; however, structure-function relationships outside of the hippocampus have not yet been explored. In this study, we examined the relationships between viscoelasticity of both the hippocampus and the orbitofrontal cortex and performance on behavioral assessments of relational memory and fluid intelligence. In a sample of healthy, young adults (N = 53), there was a significant, positive relationship between orbitofrontal cortex viscoelasticity and fluid intelligence performance (r = 0.42; p = .002). This finding is consistent with the previously reported relationship between hippocampal viscoelasticity and relational memory performance (r = 0.41; p = .002). Further, a significant double dissociation between the orbitofrontal-fluid intelligence relationship and the hippocampal-relational memory relationship was observed. These data support the specificity of regional brain MRE measures in support of separable cognitive functions. This report of a structure-function relationship observed with MRE beyond the hippocampus suggests a future role for MRE as a sensitive neuroimaging technique for brain mapping.

Glial Tissue Mechanics and Mechanosensing by Glial Cells

Understanding the mechanical behavior of human brain is critical to interpret the role of physical stimuli in both normal and pathological processes that occur in CNS tissue, such as development, inflammation, neurodegeneration, aging, and most common brain tumors. Despite clear evidence that mechanical cues influence both normal and transformed brain tissue activity as well as normal and transformed brain cell behavior, little is known about the links between mechanical signals and their biochemical and medical consequences. A multi-level approach from whole organ rheology to single cell mechanics is needed to understand the physical aspects of human brain function and its pathologies. This review summarizes the latest achievements in the field.

Perfusion alters stiffness of deep gray matter

Viscoelastic properties of the brain reflect tissue architecture at multiple length scales. However, little is known about the relation between vital tissue functions, such as perfusion, and the macroscopic mechanical properties of cerebral tissue. In this study, arterial spin labelling is paired with magnetic resonance elastography to investigate the relationship between tissue stiffness and cerebral blood flow (CBF) in the in vivo human brain. The viscoelastic modulus, | G*|, and CBF were studied in deep gray matter (DGM) of 14 healthy male volunteers in the following sub-regions: putamen, nucleus accumbens, hippocampus, thalamus, globus pallidus, and amygdala. CBF was further normalized by vessel area data to obtain the flux rate q which is proportional to the perfusion pressure gradient. The striatum (represented by putamen and nucleus accumbens) was distinct from the other DGM regions by displaying markedly higher stiffness and perfusion values. q was a predictive marker for DGM stiffness as analyzed by linear regression | G*| =  q·(4.2 ± 0.6)kPa·s + (0.80 ± 0.06)kPa ( R²= 0.92, P = 0.006). These results suggest a high sensitivity of MRE in DGM to perfusion pressure. The distinct mechano-vascular properties of striatum tissue, as compared to the rest of DGM, may reflect elevated perfusion pressure, which could explain the well-known susceptibility of the putamen to hemorrhages.

Combining viscoelasticity, diffusivity and volume of the hippocampus for the diagnosis of Alzheimer's disease based on magnetic resonance imaging

Dementia due to Alzheimer's Disease (AD) is a neurodegenerative disease for which treatment strategies at an early stage are of great clinical importance. So far, there is still a lack of non-invasive diagnostic tools to sensitively detect AD in early stages and to predict individual disease progression. Magnetic resonance elastography (MRE) of the brain may be a promising novel tool. In this proof-of-concept study, we investigated whether multifrequency-MRE (MMRE) can detect differences in hippocampal stiffness between patients with clinical diagnosis of dementia due to AD and healthy controls (HC). Further, we analyzed if the combination of three MRI-derived parameters, i.e., hippocampal stiffness, hippocampal volume and mean diffusivity (MD), improves diagnostic accuracy.

Diagnostic criteria for probable dementia due to AD were in line with the NINCDS-ADRDA criteria and were verified through history-taking (patient and informant), neuropsychological testing, routine blood results and routine MRI to exclude other medical causes of a cognitive decline.

21 AD patients and 21 HC (median age 75 years) underwent MMRE and structural MRI, from which hippocampal volume and MD were calculated. From the MMRE-images maps of the magnitude |G*| and phase angle φ of the complex shear modulus were reconstructed using multifrequency inversion. Median values of |G*| and φ were extracted within three regions of interest (hippocampus, thalamus and whole brain white matter). To test the predictive value of the main outcome parameters, we performed receiver operating characteristic (ROC) curve analyses.

Hippocampal stiffness (|G*|) and viscosity (φ) were significantly lower in the patient group (both p < 0.001). ROC curve analyses showed an area under the curve (AUC) for | G*| of 0.81 [95%CI 0.68–0.94]; with sensitivity 86%, specificity 67% for cutoff at |G*| = 980 Pa) and for φ an AUC of 0.79 [95%CI 0.66–0.93]. In comparison, the AUC of MD and hippocampal volume were 0.83 [95%CI 0.71–0.95] and 0.86 [95%CI 0.74–0.97], respectively. A combined ROC curve of |G*|, MD and hippocampal volume yielded a significantly improved AUC of 0.90 [95%CI 0.81–0.99].

In conclusion, we demonstrated reduced hippocampal stiffness and reduced hippocampal viscosity, as determined by MMRE, in patients with clinical diagnosis of dementia of the AD type. Diagnostic sensitivity was further improved by the combination with two other MRI-based hippocampal parameters. These findings motivate further investigation whether MMRE can detect decreased brain stiffness already in pre-dementia stages, and whether these changes predict cognitive decline.

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Non-invasive methods for early detection of AD are lacking.

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MRE of the brain is a promising new non-invasive diagnostic tool.

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We demonstrate reduced hippocampal stiffness (|G*|) in AD patients.

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|G*| distinguishes healthy and demented with 86% sensitivity and 67% specificity.

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Combining hippocampal stiffness, MD and volume improved diagnostic accuracy.

Bulging brains

Brain swelling is a serious condition associated with an accumulation of fluid inside the brain that can be caused by trauma, stroke, infection, or tumors. It increases the pressure inside the skull and reduces blood and oxygen supply. To relieve the intracranial pressure, neurosurgeons remove part of the skull and allow the swollen brain to bulge outward, a procedure known as decompressive craniectomy. Decompressive craniectomy has been preformed for more than a century; yet, its effects on the swollen brain remain poorly understood. Here we characterize the deformation, strain, and stretch in bulging brains using the nonlinear field theories of mechanics. Our study shows that even small swelling volumes of 28 to 56 ml induce maximum principal strains in excess of 30%. For radially outward-pointing axons, we observe maximal normal stretches of 1.3 deep inside the bulge and maximal tangential stretches of 1.3 around the craniectomy edge. While the stretch magnitude varies with opening site and swelling region, our study suggests that the locations of maximum stretch are universally shared amongst all bulging brains. Our model has the potential to inform neurosurgeons and rationalize the shape and position of the skull opening, with the ultimate goal to reduce brain damage and improve the structural and functional outcomes of decompressive craniectomy in trauma patients.

MR elastography of the brain and its application in neurological diseases

Magnetic resonance elastography (MRE) is an imaging technique for noninvasively and quantitatively assessing tissue stiffness, akin to palpation. MRE is further able assess the mechanical properties of tissues that cannot be reached by hand including the brain. The technique is a three-step process beginning with the introduction of shear waves into the tissue of interest by applying an external vibration. Next, the resulting motion is imaged using a phase-contrast MR pulse sequence with motion encoding gradients that are synchronized to the vibration. Finally, the measured displacement images are mathematically inverted to compute a map of the estimated stiffness. In the brain, the technique has demonstrated strong test-retest repeatability with typical errors of 1% for measuring global stiffness, 2% for measuring stiffness in the lobes of the brain, and 3-7% for measuring stiffness in subcortical gray matter. In healthy volunteers, multiple studies have confirmed that stiffness decreases with age, while more recent studies have demonstrated a strong relationship between viscoelasticity and behavioral performance. Furthermore, several studies have demonstrated the sensitivity of brain stiffness to neurodegeneration, as stiffness has been shown to decrease in multiple sclerosis and in several forms of dementia. Moreover, the spatial pattern of stiffness changes varies among these different classes of dementia. Finally, MRE is a promising tool for the preoperative assessment of intracranial tumors, as it can measure both tumor consistency and adherence to surrounding tissues. These factors are important predictors of surgical difficulty. In brief, MRE demonstrates potential value in a number of neurological diseases. However, significant opportunity remains to further refine the technique and better understand the underlying physiology.

Magnetic resonance elastography for examining developmental changes in the mechanical properties of the brain

Magnetic resonance elastography (MRE) is a quantitative imaging technique for noninvasively characterizing tissue mechanical properties, and has recently emerged as a valuable tool for neuroimaging. The measured mechanical properties reflect the microstructural composition and organization of neural tissue, and have shown significant effects in many neurological conditions and normal, healthy aging, and evidence has emerged supporting novel relationships between mechanical structure and cognitive function. The sensitivity of MRE to brain structure, function, and health make it an ideal technique for studying the developing brain; however, brain MRE studies on children and adolescents have only just begun. In this article, we review brain MRE and its findings, discuss its potential role in developmental neuroimaging, and provide suggestions for researchers interested in adopting this technique.

A Comparison of Finite Element-Based Inversion Algorithms, Local Frequency Estimation, and Direct Inversion Approach Used in MRE

In quantitative elastography, maps of the mechanical properties of soft tissue, or elastograms, are calculated from the measured displacement data by solving an inverse problem. The model assumptions have a significant effect on elastograms. Motivated by the high sensitivity of imaging results to the model assumptions for in vivo magnetic resonance elastography of the prostate, we compared elastograms obtained with four different methods. Two finite-element method (FEM)-based methods developed by our group were compared with two other commonly used methods, local frequency estimator (LFE) and curl-based direct inversion (c-DI). All the methods assume a linear isotropic elastic model, but the methods vary in their assumptions, such as local homogeneity or incompressibility, and in the specific approach used. We report results using simulations, phantom, and ex vivo and in vivo data. The simulation and phantom studies show, for regions with an inclusion, that the contrast to noise ratio (CNR) for the FEM methods is about three to five times higher than the CNR for the LFE and c-DI and the rms error is about half. The LFE method produces very smooth results (i.e., low CNR) and is fast. c-DI is faster than the FEM methods but it is only accurate in areas where elasticity variations are small. The artifacts resulting from the homogeneity assumption in c-DI is detrimental in regions with large variations. The ex vivo and in vivo results also show similar trends as the simulation and phantom studies. The c-FEM method is more sensitive to noise compared with the mixed-FEM due to higher orders derivatives. This is especially evident at lower frequencies, where the wave curvature is smaller and it is more prone to such error, causing a discrepancy in the absolute values between the mixed-FEM and c-FEM in our in vivo results. In general, the proposed FEMs use fewer simplifying assumptions and outperform the other methods but they are computationally more expensive.

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.

Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling

Brain magnetic resonance image segmentation is one of the most important tasks in medical image analysis and has considerable importance to the effective use of medical imagery in clinical and surgical setting. In particular, the tissue segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain measurement and disease diagnosis. A variety of studies have shown that the learning-based techniques are efficient and effective in brain tissue segmentation. However, the learning-based segmentation methods depend largely on the availability of good training labels. The commonly used 3T magnetic resonance (MR) images have insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF, therefore not able to provide good training labels for learning-based methods. The advances in ultra-high field 7T imaging make it possible to acquire images with an increasingly high level of quality. In this study, we propose an algorithm based on random forest for segmenting 3T MR images by introducing the segmentation information from their corresponding 7T MR images (through semi-automatic labeling). Furthermore, our algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers to improve the tissue segmentation. Experimental results on 10 subjects with both 3T and 7T MR images in a leave-one-out validation, show that the proposed algorithm performs much better than the state-of-the-art segmentation methods.

Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling

PURPOSE

Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio.

METHODS

In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation.

RESULTS

The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values < 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods.

CONCLUSIONS

The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.