Stiffness reconstruction methods for MR elastography

Clinical application of magnetic resonance elastography in pediatric neurological disorders

Magnetic resonance elastography is a relatively new, rapidly evolving quantitative magnetic resonance imaging technique which can be used for mapping the viscoelastic mechanical properties of soft tissues. MR elastography measurements are akin to manual palpation but with the advantages of both being quantitative and being useful for regions which are not available for palpation, such as the human brain. MR elastography is noninvasive, well tolerated, and complements standard radiological and histopathological studies by providing in vivo measurements that reflect tissue microstructural integrity. While brain MR elastography studies in adults are becoming frequent, published studies on the utility of MR elastography in children are sparse. In this review, we have summarized the major scientific principles and recent clinical applications of brain MR elastography in diagnostic neuroscience and discuss avenues for impact in assessing the pediatric brain.

Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography

Magnetic resonance elastography (MRE) is a medical imaging modality that non-invasively quantifies tissue stiffness (elasticity) and is commonly used for diagnosing liver fibrosis. Constructing an elasticity map of tissue requires solving an inverse problem involving a partial differential equation (PDE). Current numerical techniques to solve the inverse problem are noise-sensitive and require explicit specification of physical relationships. In this work, we apply physics-informed neural networks to solve the inverse problem of tissue elasticity reconstruction. Our method does not rely on numerical differentiation and can be extended to learn relevant correlations from anatomical images while respecting physical constraints. We evaluate our approach on simulated data and in vivo data from a cohort of patients with non-alcoholic fatty liver disease (NAFLD). Compared to numerical baselines, our method is more robust to noise and more accurate on realistic data, and its performance is further enhanced by incorporating anatomical information.

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.

A wavelet neural operator based elastography for localization and quantification of tumors

BACKGROUND AND OBJECTIVES

The application of intelligent imaging techniques and deep learning in the field of computer-aided diagnosis and medical imaging have improved and accelerated the early diagnosis of many diseases. Elastography is an imaging modality where an inverse problem is solved to extract the elastic properties of tissues and subsequently mapped to anatomical images for diagnostic purposes. In the present work, we propose a wavelet neural operator-based approach for correctly learning the non-linear mapping of elastic properties directly from measured displacement field data.

METHODS

The proposed framework learns the underlying operator behind the elastic mapping and thus can map any displacement data from a family to the elastic properties. The displacement fields are first uplifted to a high-dimensional space using a fully connected neural network. On the lifted data, certain iterations are performed using wavelet neural blocks. In each wavelet neural block, the lifted data are decomposed into low, and high-frequency components using wavelet decomposition. To learn the most relevant patterns and structural information from the input, the neural network kernels are directly convoluted with the outputs of the wavelet decomposition. Thereafter the elasticity field is reconstructed from the outputs from convolution. The mapping between the displacement and the elasticity using wavelets is unique and remains stable during training.

RESULTS

The proposed framework is tested on several artificially fabricated numerical examples, including a benign-cum-malignant tumor prediction problem. The trained model was also tested on real Ultrasound-based elastography data to demonstrate the applicability of the proposed scheme in clinical usage. The proposed framework reproduces the highly accurate elasticity field directly from the displacement inputs.

CONCLUSIONS

The proposed framework circumvents different data pre-processing and intermediate steps utilized in traditional methods, hence providing an accurate elasticity map. The computationally efficient framework requires fewer epochs for training, which bodes well for its clinical usability for real-time predictions. The weights and biases from pre-trained models can also be employed for transfer learning, which reduces the effective training time with random initialization.

Asynchronous magnetic resonance elastography: Shear wave speed reconstruction using noise correlation of incoherent waves

PURPOSE

The noninvasive measurement of biological tissue elasticity is an evolving technology that enables the robust characterization of soft tissue mechanics for a wide array of biomedical engineering and clinical applications. We propose, design, and implement here a new MRI technique termed asynchronous magnetic resonance elastography (aMRE) that pushes the measurement technology toward a driverless implementation. This technique can be added to clinical MRI scanners without any additional specialized hardware.

THEORY

Asynchronous MRE is founded on the theory of diffuse wavefields and noise correlation previously developed in ultrasound to reconstruct shear wave speeds using seemingly incoherent wavefields. Unlike conventional elastography methods that solve an inverse problem, aMRE directly reconstructs a pixel-wise mapping of wave speed using the spatial-temporal statistics of the measured wavefield.

METHODS

Incoherent finger tapping served as the wave-generating source for all aMRE measurements. Asynchronous MRE was performed on a phantom using a Siemens Prismafit as an experimental validation of the theory. It was further performed on thigh muscles as a proof-of-concept implementation of in vivo imaging using a Siemens Skyra scanner.

RESULTS

Numerical and phantom experiments show an accurate reconstruction of wave speeds from seemingly noisy wavefields. The proof-of-concept thigh experiments also show that the aMRE protocol can reconstruct a pixel-wise mapping of wave speeds.

CONCLUSION

Asynchronous MRE is shown to accurately reconstruct shear wave speeds in phantom experiments and remains at the proof-of-concept stage for in vivo imaging. After further validation and improvements, it has the potential to lower both the technical and monetary barriers of entry to measuring tissue elasticity.

Ensemble Kalman inversion for magnetic resonance elastography

Magnetic resonance elastography (MRE) is an MRI-based diagnostic method for measuring mechanical properties of biological tissues. MRE measurements are processed by an inversion algorithm to produce a map of the biomechanical properties. In this paper a new and powerful method (ensemble Kalman inversion with level sets (EKI)) of MRE inversion is proposed and tested. The method has critical advantages: material property variation at disease boundaries can be accurately identified, and uncertainty of the reconstructed material properties can be evaluated by consequence of the probabilistic nature of the method. EKI is tested in 2D and 3D experiments with synthetic MRE data of the human kidney. It is demonstrated that the proposed inversion method is accurate and fast.

Improving in situ acoustic intensity estimates using MR acoustic radiation force imaging in combination with multifrequency MR elastography

PURPOSE

Magnetic resonance acoustic radiation force imaging (MR-ARFI) enables focal spot localization during nonablative transcranial ultrasound therapies. As the acoustic radiation force is proportional to the applied acoustic intensity, measured MR-ARFI displacements could potentially be used to estimate the acoustic intensity at the target. However, variable brain stiffness is an obstacle. The goal of this study was to develop and assess a method to accurately estimate the acoustic intensity at the focus using MR-ARFI displacements in combination with viscoelastic properties obtained with multifrequency MR elastography (MRE).

METHODS

Phantoms with a range of viscoelastic properties were fabricated, and MR-ARFI displacements were acquired within each phantom using multiple acoustic intensities. Voigt model parameters were estimated for each phantom based on storage and loss moduli measured using multifrequency MRE, and these were used to predict the relationship between acoustic intensity and measured displacement.

RESULTS

Using assumed viscoelastic properties, MR-ARFI displacements alone could not accurately estimate acoustic intensity across phantoms. For example, acoustic intensities were underestimated in phantoms stiffer than the assumed stiffness and overestimated in phantoms softer than the assumed stiffness. This error was greatly reduced using individualized viscoelasticity measurements obtained from MRE.

CONCLUSION

We demonstrated that viscoelasticity information from MRE could be used in combination with MR-ARFI displacements to obtain more accurate estimates of acoustic intensity. Additionally, Voigt model viscosity parameters were found to be predictive of the relaxation rate of each phantom's time-varying displacement response, which could be used to optimize patient-specific MR-ARFI pulse sequences.

Fast acquisition of propagating waves in humans with low-field MRI: Toward accessible MR elastography

Most commonly used at clinical magnetic fields (1.5 to 3 T), magnetic resonance elastography (MRE) captures mechanical wave propagation to reconstruct the mechanical properties of soft tissue with MRI. However, in terms of noninvasively assessing disease progression in a broad range of organs (e.g., liver, breast, skeletal muscle, and brain), its accessibility is limited and its robustness is challenged when magnetic susceptibility differences are encountered. Low-field MRE offers an opportunity to overcome these issues, and yet it has never been demonstrated in vivo in humans with magnetic fields <1.5 T mainly because of the long acquisition times required to achieve a sufficient signal-to-noise ratio. Here, we describe a method to accelerate 3D motion-sensitized MR scans at 0.1 T using only 10% k-space sampling combined with a high-performance detector and an efficient encoding acquisition strategy. Its application is demonstrated in vivo in the human forearm for a single motion-encoding direction in less than 1 min.

Numerical simulation of wave propagation through interfaces using the extended finite element method for magnetic resonance elastography

Magnetic resonance elastography (MRE) is an elasticity imaging technique for quantitatively assessing the stiffness of human tissues. In MRE, finite element method (FEM) is widely used for modeling wave propagation and stiffness reconstruction. However, in front of inclusions with complex interfaces, FEM can become burdensome in terms of the model partition and computationally expensive. In this work, we implement a formulation of FEM, known as the eXtended finite element method (XFEM), which is a method used for modeling discontinuity like crack and heterogeneity. Using a level-set method, it makes the interface independent of the mesh, thus relieving the meshing efforts. We investigate this method in two studies: wave propagation across an oblique linear interface and stiffness reconstruction of a random-shape inclusion. In the first study, numerical results by XFEM and FEM models revealing the wave conversion rules at linear interface are presented and successfully compared to the theoretical predictions. The second study, investigated in a pseudo-practical application, demonstrates further the applicability of XFEM in MRE and the convenience, accuracy, and speed of XFEM with respect to FEM. XFEM can be regarded as a promising alternative to FEM for inclusion modeling in MRE.

Mechanobiology of Colorectal Cancer

In this review, the mechanobiology of colorectal cancer (CRC) are discussed. Mechanotransduction of CRC is addressed considering the relationship of several biophysical cues and biochemical pathways. Mechanobiology is focused on considering how it may influence epithelial cells in terms of motility, morphometric changes, intravasation, circulation, extravasation, and metastization in CRC development. The roles of the tumor microenvironment, ECM, and stroma are also discussed, taking into account the influence of alterations and surface modifications on mechanical properties and their impact on epithelial cells and CRC progression. The role of cancer-associated fibroblasts and the impact of flow shear stress is addressed in terms of how it affects CRC metastization. Finally, some insights concerning how the knowledge of biophysical mechanisms may contribute to the development of new therapeutic strategies and targeting molecules and how mechanical changes of the microenvironment play a role in CRC disease are presented.

The effect of mechanical force in genitourinary malignancies

INTRODUCTION

Mechanical force is attributed to the formation of tumor blood vessels, influences cancer cell invasion and metastasis, and promotes reprogramming of the energy metabolism. Currently, therapy strategies for the tumor microenvironment are being developed progressively. The purpose of this article is to discuss the molecular mechanism, diagnosis, and treatment of mechanical force in urinary tract cancers and outline the medications used in the mechanical microenvironment.

AREAS COVERED

This review covers the complex mechanical elements in the microenvironment of urinary system malignancies, focusing on mechanical molecular mechanisms for diagnosis and treatment.

EXPERT OPINION

The classification of various mechanical forces, such as matrix stiffness, shear force, and other forces, is relatively straightforward. However, little is known about the molecular process of mechanical forces in urinary tract malignancies. Because mechanical therapy is still controversial, it is critical to understand the molecular basis of mechanical force before adding mechanical therapy solutions.

A novel technique for automating stiffness measurement and emphasizing the main wave: Coherent-wave auto-selection (CHASE)

This study aims to develop and assess a new automated processing technique in MR elastography (MRE), namely coherent-wave auto-selection (CHASE). CHASE enables automatic selection of the region of interest (ROI) for stiffness measurement by extraction of the coherent wave region (CHASE ROI), and it improves the reconstruction of stiffness by a directional filter oriented along the main wave in each pixel (CHASE filtering). In this study, MRE of a phantom and of the liver of four healthy volunteers was performed. To investigate the potential of CHASE, this study assessed the CHASE according to three indices through the phantom study: 1) agreement on the ROI settings between CHASE and expert observers, 2) noise dependency, and 3) effect of the CHASE on stiffness variability within the CHASE ROI. The agreements on the ROI settings were analyzed by Cohen's kappa coefficient (κ). The noise dependency was analyzed by the mean absolute percentage errors (MAPEs) within the ROI between low (20%-80% amplitudes) and high vibration amplitudes (100% amplitude). The stiffness variability was assessed by standard deviation (SD) within the ROI. In the volunteer study, agreements on the ROI settings (or stiffness value) and stiffness variability within the CHASE ROI were assessed using κ-value (or intraclass correlation coefficient: ICC) and coefficient of variation, respectively. The results showed close agreement on the ROI settings and stiffness (κ-value: greater than 0.61 in both the phantom and volunteer studies, ICC: 0.97 in the volunteer study). The MAPEs within the CHASE ROI were much smaller than those in the whole region of the phantom (CHASE ROI vs. the whole region at 20% amplitude: 10.3% vs. 50.8%). Moreover, in both the phantom and volunteer studies, the stiffness variation within the CHASE ROI was smaller in the elastogram processed with CHASE filtering than in the unprocessed one. Our results demonstrated that the CHASE has high robustness against noise and the potential to provide ROI settings for stiffness measurement comparable to expert observers, as well as improve the reconstruction of stiffness.

Magnetic Resonance Elastography Reconstruction for Anisotropic Tissues

Elastography has become widely used clinically for characterising changes in soft tissue mechanics that are associated with altered tissue structure and composition. However, some soft tissues, such as muscle, are not isotropic as is assumed in clinical elastography implementations. This limits the ability of these methods to capture changes in anisotropic tissues associated with disease. The objective of this study was to develop and validate a novel elastography reconstruction technique suitable for estimating the linear viscoelastic mechanical properties of transversely isotropic soft tissues. We derived a divergence-free formulation of the governing equations for acoustic wave propagation through a linearly transversely isotropic viscoelastic material, and transformed this into a weak form. This was then implemented into a finite element framework, enabling the analysis of wave input data and tissue structural fibre orientations, in this case based on diffusion tensor imaging. To validate the material constants obtained with this method, numerous in silico phantom experiments were run which encompassed a range of variations in wave input directions, material properties, fibre structure and noise. The method was also tested on ex vivo muscle and in vivo human volunteer calf muscles, and compared with a previous curl-based inversion method. The new method robustly extracted the transversely isotropic shear moduli (G_⊥^', G_∥^', G^″) from the in silico phantom tests with minimal bias, including in the presence of experimentally realistic levels of noise in either fibre orientation or wave data. This new method performed better than the previous method in the presence of noise. Anisotropy estimates from the ex vivo muscle phantom agreed well with rheological tests. In vivo experiments on human calf muscles were able to detect increases in muscle shear moduli with passive muscle stretch. This new reconstruction method can be applied to quantify tissue mechanical properties of anisotropic soft tissues, such as muscle, in health and disease.

MR elastography inversion by compressive recovery

Direct inversion (DI) derives tissue shear modulus by inverting the Helmholtz equation. However, conventional DI is sensitive to data quality due to the ill-posed nature of Helmholtz inversion and thus providing reliable stiffness estimation can be challenging. This becomes more problematic in the case of estimating shear stiffness of the lung in which the low tissue density and short T2* result in considerably low signal-to-noise ratio during lung MRE. In the present study, we propose to perform MRE inversion by compressive recovery (MICRo). Such a technique aims to improve the numerical stability and the robustness to data noise of Helmholtz inversion by using prior knowledge on data noise and transform sparsity of the stiffness map. The developed inversion strategy was first validated in simulated phantoms with known stiffness. Next, MICRo was compared to the standard clinical multi-modal DI (MMDI) method forin vivoliver MRE in healthy subjects and patients with different stages of liver fibrosis. After establishing the accuracy of MICRo, we demonstrated the robustness of the proposed technique against data noise in lung MRE with healthy subjects. In simulated phantoms with single-directional or multi-directional waves, MICRo outperformed DI with Romano filter or Savitsky and Golay filter, especially when the stiffness and/or noise level was high. In hepatic MRE application, agreement was observed between MICRo and MMDI. Measuringin vivolung stiffness, MICRo demonstrated its advantages over filtered DI by yielding stable stiffness estimation at both residual volume and total lung capacity. These preliminary results demonstrate the potential value of the proposed technique and also warrant further investigation in a larger clinical population.

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.

Targeted Blood Brain Barrier Opening With Focused Ultrasound Induces Focal Macrophage/Microglial Activation in Experimental Autoimmune Encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is a model of multiple sclerosis (MS). EAE reflects important histopathological hallmarks, dissemination, and diversity of the disease, but has only moderate reproducibility of clinical and histopathological features. Focal lesions are less frequently observed in EAE than in MS, and can neither be constrained to specific locations nor timed to occur at a pre-specified moment. This renders difficult any experimental assessment of the pathogenesis of lesion evolution, including its inflammatory, degenerative (demyelination and axonal degeneration), and reparatory (remyelination, axonal sprouting, gliosis) component processes. We sought to develop a controlled model of inflammatory, focal brain lesions in EAE using focused ultrasound (FUS). We hypothesized that FUS induced focal blood brain barrier disruption (BBBD) will increase the likelihood of transmigration of effector cells and subsequent lesion occurrence at the sonicated location. Lesion development was monitored with conventional magnetic resonance imaging (MRI) as well as with magnetic resonance elastography (MRE) and further analyzed by histopathological means. EAE was induced in 12 6-8 weeks old female C57BL/6 mice using myelin oligodendrocyte glycoprotein (MOG) peptide. FUS-induced BBBD was performed 6, 7, and 9 days after immunization in subgroups of four animals and in an additional control group. MRI and MRE were performed on a 7T horizontal bore small animal MRI scanner. Imaging was conducted longitudinally 2 and 3 weeks after disease induction and 1 week after sonication in control animals, respectively. The scan protocol comprised contrast-enhanced T1-weighted and T2-weighted sequences as well as MRE with a vibration frequency of 1 kHz. Animals were sacrificed for histopathology after the last imaging time point. The overall clinical course of EAE was mild. A total of seven EAE animals presented with focal T2w hyperintense signal alterations in the sonicated hemisphere. These were most frequent in the group of animals sonicated 9 days after immunization. Histopathology revealed foci of activated microglia/macrophages in the sonicated right hemisphere of seven EAE animals. Larger cellular infiltrates or apparent demyelination were not seen. Control animals showed no abnormalities on MRI and did not have clusters of activated microglia/macrophages at the sites targeted with FUS. None of the animals had hemorrhages or gross tissue damage as potential side effects of FUS. EAE-animals tended to have lower values of viscoelasticity and elasticity in the sonicated compared to the contralateral parenchyma. This trend was significant when comparing the right sonicated to the left normal hemisphere and specifically the right sonicated compared to the left normal cortex in animals that underwent FUS-BBBD 9 days after immunization (right vs. left hemisphere: mean viscoelasticity 6.1 vs. 7.2 kPa; p = 0.003 and mean elasticity 4.9 vs. 5.7 kPa, p = 0.024; right vs. left cortex: mean viscoelasticity 5.8 vs. 7.5 kPa; p = 0.004 and mean elasticity 5 vs. 6.5 kPa; p = 0.008). A direct comparison of the biomechanical properties of focal T2w hyperintensities with normal appearing brain tissue did not yield significant results. Control animals showed no differences in viscoelasticity between sonicated and contralateral brain parenchyma. We here provide first evidence for a controlled lesion induction model in EAE using FUS-induced BBBD. The observed lesions in EAE are consistent with foci of activated microglia that may be interpreted as targeted initial inflammatory activity and which have been described as pre-active lesions in MS. Such foci can be identified and monitored with MRI. Moreover, the increased inflammatory activity in the sonicated brain parenchyma seems to have an effect on overall tissue matrix structure as reflected by changes of biomechanical parameters.

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.

Shear wave cardiovascular MR elastography using intrinsic cardiac motion for transducer-free non-invasive evaluation of myocardial shear wave velocity

Changes in myocardial stiffness may represent a valuable biomarker for early tissue injury or adverse remodeling. In this study, we developed and validated a novel transducer-free magnetic resonance elastography (MRE) approach for quantifying myocardial biomechanics using aortic valve closure-induced shear waves. Using motion-sensitized two-dimensional pencil beams, septal shear waves were imaged at high temporal resolution. Shear wave speed was measured using time-of-flight of waves travelling between two pencil beams and corrected for geometrical biases. After validation in phantoms, results from twelve healthy volunteers and five cardiac patients (two left ventricular hypertrophy, two myocardial infarcts, and one without confirmed pathology) were obtained. Torsional shear wave speed in the phantom was 3.0 ± 0.1 m/s, corresponding with reference speeds of 2.8 ± 0.1 m/s. Geometrically-biased flexural shear wave speed was 1.9 ± 0.1 m/s, corresponding with simulation values of 2.0 m/s. Corrected septal shear wave speeds were significantly higher in patients than healthy volunteers [14.1 (11.0-15.8) m/s versus 3.6 (2.7-4.3) m/s, p = 0.001]. The interobserver 95%-limits-of-agreement in healthy volunteers were ± 1.3 m/s and interstudy 95%-limits-of-agreement - 0.7 to 1.2 m/s. In conclusion, myocardial shear wave speed can be measured using aortic valve closure-induced shear waves, with cardiac patients showing significantly higher shear wave speeds than healthy volunteers. This non-invasive measure may provide valuable insights into the pathophysiology of heart failure.

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

BACKGROUND

Changes in brain stiffness can be an important biomarker for neurological disease. Magnetic resonance elastography (MRE) quantifies tissue stiffness, but the results vary between acquisition and reconstruction methods.

PURPOSE

To measure MRE repeatability and estimate the effect of different reconstruction methods and varying data quality on estimated brain stiffness.

STUDY TYPE

Prospective.

SUBJECTS

Fifteen healthy subjects.

FIELD STRENGTH/SEQUENCE

3T MRI, gradient-echo elastography sequence with a 50 Hz vibration frequency.

ASSESSMENT

Imaging was performed twice in each subject. Images were reconstructed using a curl-based and a finite-element-model (FEM)-based method. Stiffness was measured in the whole brain, in white matter, and in four cortical and four deep gray matter regions. Repeatability coefficients (RC), intraclass correlation coefficients (ICC), and coefficients of variation (CV) were calculated. MRE data quality was quantified by the ratio between shear waves and compressional waves.

STATISTICAL TESTS

Median values with range are presented. Reconstruction methods were compared using paired Wilcoxon signed-rank tests, and Spearman's rank correlation was calculated between MRE data quality and stiffness. Holm-Bonferroni corrections were employed to adjust for multiple comparisons.

RESULTS

In the whole brain, CV was 4.3% and 3.8% for the curl and the FEM reconstruction, respectively, with 4.0-12.8% for subregions. Whole-brain ICC was 0.60-0.74, ranging from 0.20 to 0.89 in different regions. RC for the whole brain was 0.14 kPa and 0.17 kPa for the curl and FEM methods, respectively. FEM reconstruction resulted in 39% higher stiffness than the curl reconstruction (P < 0.05). MRE data quality, defined as shear-compression wave ratio, was higher in peripheral regions than in central regions of the brain (P < 0.05). No significant correlations were observed between MRE data quality and stiffness estimates.

DATA CONCLUSION

MRE of the human brain is a robust technique in terms of repeatability. Caution is warranted when comparing stiffness values obtained with different techniques.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Magnetic resonance elastography for estimating in vivo stiffness of the abdominal aorta using cardiac-gated spin-echo echo-planar imaging: a feasibility study

INTRODUCTION

Magnetic resonance elastography (MRE)-derived aortic stiffness is a potential biomarker for multiple cardiovascular diseases. Currently, gradient-recalled echo (GRE) MRE is a widely accepted technique to estimate aortic stiffness. However, multi-slice GRE MRE requires multiple breath-holds (BHs), which can be challenging for patients who cannot consistently hold their breath. The aim of this study was to investigate the feasibility of a multi-slice spin-echo echo-planar imaging (SE-EPI) MRE sequence for quantifying in vivo aortic stiffness using a free-breathing (FB) protocol and a single-BH protocol.

METHOD

On Scanner 1, 25 healthy subjects participated in the validation of FB SE-EPI against FB GRE. On Scanner 2, another 15 healthy subjects were recruited to compare FB SE-EPI with single-BH SE-EPI. Among all volunteers, five participants were studied on both scanners to investigate the inter-scanner reproducibility of FB SE-EPI aortic MRE. Bland-Altman analysis, Lin's concordance correlation coefficient (LCCC) and coefficient of variation (COV) were evaluated. The phase-difference signal-to-noise ratios (PD SNR) were compared.

RESULTS

Aortic MRE using FB SE-EPI and FB GRE yielded similar stiffnesses (paired t-test, P = 0.19), with LCCC = 0.97. The FB SE-EPI measurements were reproducible (intra-scanner LCCC = 0.96) and highly repeatable (LCCC = 0.99). The FB SE-EPI MRE was also reproducible across different scanners (inter-scanner LCCC = 0.96). Single-BH SE-EPI scans yielded similar stiffness to FB SE-EPI scans (LCCC = 0.99) and demonstrated a low COV of 2.67% across five repeated measurements.

CONCLUSION

Multi-slice SE-EPI aortic MRE using an FB protocol or a single-BH protocol is reproducible and repeatable with advantage over multi-slice FB GRE in reducing acquisition time. Additionally, FB SE-EPI MRE provides a potential alternative to BH scans for patients who have challenges in holding their breath.

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.

Tissue Rheology as a Possible Complementary Procedure to Advance Histological Diagnosis of Colon Cancer

In recent years, rheological measurements of cells and tissues at physiological and pathological stages have become an essential method to determine how forces and changes in mechanical properties contribute to disease development and progression, but there is no standardization of this procedure so far. In this study, we evaluate the potential of nanoscale atomic force microscopy (AFM) and macroscopic shear rheometry to assess the mechanical properties of healthy and cancerous human colon tissues. The direct comparison of tissue mechanical behavior under uniaxial and shear deformation shows that cancerous tissues not only are stiffer compared to healthy tissue but also respond differently when shear and compressive stresses are applied. These results suggest that rheological parameters can be useful measures of colon cancer mechanopathology. Additionally, we extend the list of biological materials exhibiting compressional stiffening and shear weakening effects to human colon tumors. These mechanical responses might be promising mechanomarkers and become part of the new procedures in colon cancer diagnosis. Enrichment of histopathological grading with rheological assessment of tissue mechanical properties will potentially allow more accurate colon cancer diagnosis and improve prognosis.

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described.

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.

Magnetic resonance elastography of the supraspinatus muscle: A preliminary study on test-retest repeatability and wave quality with different frequencies and image filtering

The purpose of this study was to determine an optimal condition (vibration frequency and image filtering) for stiffness estimation with high accuracy and stiffness measurement with high repeatability in magnetic resonance elastography (MRE) of the supraspinatus muscle. Nine healthy volunteers underwent two MRE exams separated by at least a 30 min break, on the same day. MRE acquisitions were performed with a gradient-echo type multi-echo MR sequence at 75, 100, and 125 Hz pneumatic vibration. Wave images were processed by a bandpass filter or filter combining bandpass and directional filters (bandpass-directional filter). An observer specified the region of interest (ROI) on clear wave propagation in the supraspinatus muscle, within which the observer measured the stiffness. This study assessed wave image quality according to two indices, as a substitute for the assessment of the accuracy of the stiffness estimation. One is the size of the clear wave propagation area (ROI size used to measure the stiffness) and the other is the qualitative stiffness resolution score in that area. These measurements made by the observer were repeated twice at least one month apart after each MRE exam. This study assessed the intra-examiner and observer repeatability of the stiffness value, ROI size and resolution score in each combination of vibration frequency and image filter. Repeatability of the data was analyzed using the intraclass correlation coefficient (ICC) and 95% limits-of-agreement (LOA) in Bland-Altman analysis. The analyses on intra-examiner and observer repeatability of stiffness indicated that the ICC and 95% LOA were not varied greatly depending on vibration frequency and image filter (intra-examiner repeatability, ICC range, 0.79 to 0.88; 95% LOA range, ±23.95 to ±32.42%, intra-observer repeatability, ICC range, 0.98 to 1.00; 95% LOA range, ±5.10 to ±10.99%). In the analyses on intra-examiner repeatability of ROI size, ICCs were rather low (ranging from: 0.03 to 0.69) while 95% LOA was large in all the combinations of vibration frequency and image filter (ranging from: ±62.66 to ±83.33%). In the analyses on intra-observer repeatability of ROI size, ICCs were sufficiently high in the total combination of vibration frequency and image filter (ranging from 0.80 to 0.87) while the 95% LOAs were better (lower) in the bandpass-directional filter than the bandpass filter (bandpass directional filter vs. bandpass filter, ±28.81 vs. ±54.83% at 75 Hz; ±25.63 vs. ±37.83% at 100 Hz; ±34.51 vs. ±43.36% at 125 Hz). In the analyses on intra-examiner and observer repeatability of resolution score, the mean difference (bias) between the two exams (or observations) was significantly low and there was almost no difference across all the combinations of vibration frequency and image filter (range of bias: -0.11-0.11 and -0.17-0.00, respectively). Additionally, effects of vibration frequency and image filter on wave image quality (ROI size and resolution score) were assessed separately in each exam. Both mean ROI size and resolution score in the bandpass-directional filter were larger than those in the bandpass filter. Among the data in the bandpass-directional filter, mean ROI size was larger at 75 and 100 Hz, and mean resolution score was larger at 100 and 125 Hz. Taking into consideration with the results of repeatability and wave image quality, the present results suggest that optimal vibration frequency and image filter for MRE of the supraspinatus muscles is 100 Hz and bandpass-directional filter, respectively.

Towards noninvasive estimation of tumour pressure by utilising MR elastography and nonlinear biomechanical models: a simulation and phantom study

The solid and fluid pressures of tumours are often elevated relative to surrounding tissue. This increased pressure is known to correlate with decreased treatment efficacy and potentially with tumour aggressiveness and therefore, accurate noninvasive estimates of tumour pressure would be of great value. We present a proof-of-concept method to infer the total tumour pressure, that is the sum of the fluid and solid parts, by examining stiffness in the peritumoural tissue with MR elastography and utilising nonlinear biomechanical models. The pressure from the tumour deforms the surrounding tissue leading to changes in stiffness. Understanding and accounting for these biases in stiffness has the potential to enable estimation of total tumour pressure. Simulations are used to validate the method with varying pressure levels, tumour shape, tumour size, and noise levels. Results show excellent matching in low noise cases and still correlate well with higher noise. Percent error remains near or below 10% for higher pressures in all noise level cases. Reconstructed pressures were also calculated from experiments with a catheter balloon embedded in a plastisol phantom at multiple inflation levels. Here the reconstructed pressures generally match the increases in pressure measured during the experiments. Percent errors between average reconstructed and measured pressures at four inflation states are 17.9%, 52%, 23.2%, and 0.9%. Future work will apply this method to in vivo data, potentially providing an important biomarker for cancer diagnosis and treatment.

Nonlinear viscoelastic constitutive model for bovine liver tissue

Soft tissue mechanical characterisation is important in many areas of medical research. Examples span from surgery training, device design and testing, sudden injury and disease diagnosis. The liver is of particular interest, as it is the most commonly injured organ in frontal and side motor vehicle crashes, and also assessed for inflammation and fibrosis in chronic liver diseases. Hence, an extensive rheological characterisation of liver tissue would contribute to advancements in these areas, which are dependent upon underlying biomechanical models. The aim of this paper is to define a liver constitutive equation that is able to characterise the nonlinear viscoelastic behaviour of liver tissue under a range of deformations and frequencies. The tissue response to large amplitude oscillatory shear (1–50%) under varying preloads (1–20%) and frequencies (0.5–2 Hz) is modelled using viscoelastic-adapted forms of the Mooney–Rivlin, Ogden and exponential models. These models are fit to the data using classical or modified objective norms. The results show that all three models are suitable for capturing the initial nonlinear regime, with the latter two being capable of capturing, simultaneously, the whole deformation range tested. The work presented here provides a comprehensive analysis across several material models and norms, leading to an identifiable constitutive equation that describes the nonlinear viscoelastic behaviour of the liver.

Imaging brain function with simultaneous BOLD and viscoelasticity contrast: fMRI/fMRE

Magnetic resonance elastography (MRE) is emerging as a new tool for studying viscoelastic changes in the brain resulting from functional processes. Here, we demonstrate a novel time series method to generate robust functional magnetic resonance elastography (fMRE) activation maps in response to a visual task with a flashing checkerboard stimulus. Using a single-shot spin-echo (SS-SE) pulse sequence, the underlying raw images inherently contain blood-oxygen-level dependent (BOLD) contrast, allowing simultaneous generation of functional magnetic resonance imaging (fMRI) activation maps from the magnitude and functional magnetic resonance elastography (fMRE) maps from the phase. This allows an accurate comparison of the spatially localized stiffness (fMRE) and BOLD (fMRI) changes within a single scan, eliminating confounds inherent in separately acquired scans. Results indicate that tissue stiffness within the visual cortex increases 6-11% with visual stimuli, whereas the BOLD signal change was 1-2%. Furthermore, the fMRE and fMRI activation maps have strong spatial overlap within the visual cortex, providing convincing evidence that fMRE is possible in the brain. However, the fMRE temporal SNR (tSNR_fMRE) maps are heterogeneous across the brain. Using a dictionary matching approach to characterize the time series, the viscoelastic changes are consistent with a viscoelastic response function (VRF) time constant of 12.1 ​s ± 3.0 ​s for a first-order exponential decay, or a shape parameter of 8.1 ​s ± 1.4 ​s for a gamma-variate.

Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments

Analysis of invasion front has been widely used to decipher biological properties, as well as the growth dynamics of the corresponding populations. Likewise, the invasion front of tumors has been investigated, from which insights into the biological mechanisms of tumor growth have been gained. We develop a model to study how tumors' invasion front depends on the relevant properties of a cellular environment. To do so, we develop a model based on a nonlinear reaction-diffusion equation, the Fisher-Kolmogorov-Petrovsky-Piskunov equation, to model tumor growth. Our study aims to understand how heterogeneity in the cellular environment's stiffness, as well as spatial correlations in its morphology, the existence of both of which has been demonstrated by experiments, affects the properties of tumor invasion front. It is demonstrated that three important factors affect the properties of the front, namely the spatial distribution of the local diffusion coefficients, the spatial correlations between them, and the ratio of the cells' duplication rate and their average diffusion coefficient. Analyzing the scaling properties of tumor invasion front computed by solving the governing equation, we show that, contrary to several previous claims, the invasion front of tumors and cancerous cell colonies cannot be described by the well-known models of kinetic growth, such as the Kardar-Parisi-Zhang equation.

Magnetic resonance elastography of skeletal muscle deep tissue injury

The current state-of-the-art diagnosis method for deep tissue injury in muscle, a subcategory of pressure ulcers, is palpation. It is recognized that deep tissue injury is frequently preceded by altered biomechanical properties. A quantitative understanding of the changes in biomechanical properties preceding and during deep tissue injury development is therefore highly desired. In this paper we quantified the spatial-temporal changes in mechanical properties upon damage development and recovery in a rat model of deep tissue injury. Deep tissue injury was induced in nine rats by two hours of sustained deformation of the tibialis anterior muscle. Magnetic resonance elastography (MRE), T₂ -weighted, and T₂ -mapping measurements were performed before, directly after indentation, and at several timepoints during a 14-day follow-up. The results revealed a local hotspot of elevated shear modulus (from 3.30 ± 0.14 kPa before to 4.22 ± 0.90 kPa after) near the center of deformation at Day 0, whereas the T₂ was elevated in a larger area. During recovery there was a clear difference in the time course of the shear modulus and T₂ . Whereas T₂ showed a gradual normalization towards baseline, the shear modulus dropped below baseline from Day 3 up to Day 10 (from 3.29 ± 0.07 kPa before to 2.68 ± 0.23 kPa at Day 10, P < 0.001), followed by a normalization at Day 14. In conclusion, we found an initial increase in shear modulus directly after two hours of damage-inducing deformation, which was followed by decreased shear modulus from Day 3 up to Day 10, and subsequent normalization. The lower shear modulus originates from the moderate to severe degeneration of the muscle. MRE stiffness values were affected in a smaller area as compared with T₂ . Since T₂ elevation is related to edema, distributing along the muscle fibers proximally and distally from the injury, we suggest that MRE is more specific than T₂ for localization of the actual damaged area.

Imaging localized neuronal activity at fast time scales through biomechanics

Mapping neuronal activity noninvasively is a key requirement for in vivo human neuroscience. Traditional functional magnetic resonance (MR) imaging, with a temporal response of seconds, cannot measure high-level cognitive processes evolving in tens of milliseconds. To advance neuroscience, imaging of fast neuronal processes is required. Here, we show in vivo imaging of fast neuronal processes at 100-ms time scales by quantifying brain biomechanics noninvasively with MR elastography. We show brain stiffness changes of ~10% in response to repetitive electric stimulation of a mouse hind paw over two orders of frequency from 0.1 to 10 Hz. We demonstrate in mice that regional patterns of stiffness modulation are synchronous with stimulus switching and evolve with frequency. For very fast stimuli (100 ms), mechanical changes are mainly located in the thalamus, the relay location for afferent cortical input. Our results demonstrate a new methodology for noninvasively tracking brain functional activity at high speed.

Image reconstruction utilizing median filtering applied to elastography

BACKGROUND

The resources of ultrafast technology can be used to add another analysis to ultrasound imaging: assessment of tissue viscoelasticity. Ultrafast image formation can be utilized to find transitory shear waves propagating in soft tissue, which permits quantification of the mechanical properties of the tissue via elastography. This technique permits simple and noninvasive diagnosis and monitoring of disease.

METHODS

This article presents a method to estimate the viscoelastic properties and rigidity of structures using the ultrasound technique known as shear wave elasticity imaging (SWEI). The Verasonics Vantage 128 research platform and L11-4v transducer were used to acquire radio frequency signals from a model 049A elastography phantom (CIRS, USA), with subsequent processing and analysis in MATLAB.

RESULTS

The images and indexes obtained reflect the qualitative measurements of the different regions of inclusions in the phantom and the respective alterations in the viscoelastic properties of distinct areas. Comparison of the results obtained with this proposed technique and other commonly used techniques demonstrates the characteristics of median filtering in smoothing variations in velocity to form elastographic images. The results from the technique proposed in this study are within the margins of error indicated by the phantom manufacturer for each type of inclusion; for the phantom base and for type I, II, III, and IV inclusions, respectively, in kPa and percentage errors, these are 25 (24.0%), 8 (37.5%), 14 (28.6%), 45 (17.8%), and 80 (15.0%). The values obtained using the method proposed in this study and mean percentage errors were 29.18 (- 16.7%), 10.26 (- 28.2%), 15.64 (- 11.7%), 45.81 (- 1.8%), and 85.21 (- 6.5%), respectively.

CONCLUSIONS

The new technique to obtain images uses a distinct filtering function which considers the mean velocity in the region around each pixel, in turn allowing adjustments according to the characteristics of the phantom inclusions within the ultrasound and optimizing the resulting elastographic images.

Magnetic Resonance Elastography of Rodent Brain

Magnetic resonance elastography (MRE) is a non-invasive imaging technique, using the propagation of mechanical waves as a probe to palpate biological tissues. It consists in three main steps: production of shear waves within the tissue; encoding subsequent tissue displacement in magnetic resonance images; and extraction of mechanical parameters based on dedicated reconstruction methods. These three steps require an acoustic-frequency mechanical actuator, magnetic resonance imaging acquisition, and a post-processing tool for which no turnkey technology is available. The aim of the present review is to outline the state of the art of reported set-ups to investigate rodent brain mechanical properties. The impact of experimental conditions in dimensioning the set-up (wavelength and amplitude of the propagated wave, spatial resolution, and signal-to-noise ratio of the acquisition) on the accuracy and precision of the extracted parameters is discussed, as well as the influence of different imaging sequences, scanners, electromagnetic coils, and reconstruction algorithms. Finally, the performance of MRE in demonstrating viscoelastic differences between structures constituting the physiological rodent brain, and the changes in brain parameters under pathological conditions, are summarized. The recently established link between biomechanical properties of the brain as obtained on MRE and structural factors assessed by histology is also studied. This review intends to give an accessible outline on how to conduct an elastography experiment, and on the potential of the technique in providing valuable information for neuroscientists.

Magnetic resonance elastography in nonlinear viscoelastic materials under load

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

Editorial on the Special Issue of Applied Sciences on the Topic of Elastography

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