Artificial neural networks for stiffness estimation in magnetic resonance elastography

A Comparative Study of Three Systems for Liver Magnetic Resonance Elastography

BACKGROUND

Different MR elastography (MRE) systems may produce different stiffness measurements, making direct comparison difficult in multi-center investigations.

PURPOSE

To assess the repeatability and reproducibility of liver stiffness measured by three typical MRE systems.

STUDY TYPE

Prospective.

POPULATION/PHANTOMS

Thirty volunteers without liver disease history (20 males, aged 21-28)/5 gel phantoms.

FIELD STRENGTH/SEQUENCE

3.0 T United Imaging Healthcare (UIH), 1.5 T Siemens Healthcare, 3.0 T General Electric Healthcare (GE)/Echo planar imaging-based MRE sequence.

ASSESSMENT

Wave images of volunteers and phantoms were acquired by three MRE systems. Tissue stiffness was evaluated by two observers, while phantom stiffness was assessed automatically by code. The reproducibility across three MRE systems was quantified based on the mean stiffness of each volunteer and phantom.

STATISTICAL TESTS

Intraclass correlation coefficients (ICC), coefficients of variation (CV), and Bland-Altman analyses were used to assess the interobserver reproducibility, the interscan repeatability, and the intersystem reproducibility. Paired t-tests were performed to assess the interobserver and interscan variation. Friedman tests with Dunn's multiple comparison correction were performed to assess the intersystem variation. P values less than 0.05 indicated significant difference.

RESULTS

The reproducibility of stiffness measured by the two observers demonstrated consistency with ICC > 0.92, CV < 4.32%, Mean bias < 2.23%, and P > 0.06. The repeatability of measurements obtained using the electromagnetic system for the liver revealed ICC > 0.96, CV < 3.86%, Mean bias < 0.19%, P > 0.90. When considering the range of reproducibility across the three systems for liver evaluations, results ranged with ICCs from 0.70 to 0.87, CVs from 6.46% to 10.99%, and Mean biases between 1.89% and 6.30%. Phantom studies showed similar results. The values of measured stiffness differed across all three systems significantly.

DATA CONCLUSION

Liver stiffness values measured from different MRE systems can be different, but the measurements across the three MRE systems produced consistent results with excellent reproducibility.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases

Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) present a major health burden to society. Changes in brain structure and cognition are generally only observed at the late stage of the disease. Although advanced magnetic resonance imaging (MRI) techniques such as diffusion imaging may allow identification of biomarkers at earlier stages of neurodegeneration, early diagnosis is still challenging. Magnetic resonance elastography (MRE) is a noninvasive MRI technique for studying the mechanical properties of tissues by measuring the wave propagation induced in the tissues using a purpose-built actuator. Here, we present a systematic review of preclinical and clinical studies in which MRE has been applied to study neurodegenerative diseases. Actuator systems for data acquisition, inversion algorithms for data analysis, and sample demographics are described and tissue stiffness measures obtained for the whole brain and internal structures are summarized. A total of six animal studies and eight human studies have been published. The animal studies refer to 123 experimental animals (68 AD and 55 PD) and 121 wild-type animals, while the human studies refer to 142 patients with neurodegenerative disease (including 56 AD and 17 PD) and 166 controls. The animal studies are consistent in the reporting of decreased stiffness of the hippocampal region in AD mice. However, in terms of disease progression, although consistent decreases in either storage modulus or shear modulus magnitude are reported for whole brain, there is variation in the results reported for the hippocampal region. The clinical studies are consistent in reports of a significant decrease in either whole brain storage modulus or shear modulus magnitude, in both AD and PD and with different brain structures affected in different neurodegenerative diseases. MRE studies of neurodegenerative diseases are still in their infancy, and in future it will be interesting to investigate potential relationships between brain mechanical properties and clinical measures, which may help elucidate the mechanisms underlying onset and progression of neurodegenerative diseases. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

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.

Data-driven electrical conductivity brain imaging using 3 T MRI

Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignoreB 1 + magnitude information, such as Savitzky-Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications.

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.

Features of Idiopathic Intracranial Hypertension on MRI With MR Elastography: Prospective Comparison With Control Individuals and Assessment of Postintervention Changes

BACKGROUND. Understanding of dynamic changes of MRI findings in response to intracranial pressure (ICP) changes in idiopathic intracranial hypertension (IIH) is limited. Brain stiffness, as assessed by MR elastography (MRE), may reflect changes in ICP. OBJECTIVE. The purpose of this study was to compare pituitary height, ventricular size, and brain stiffness between patients with IIH and control individuals and to evaluate for changes in these findings in patients with IIH after interventions to reduce ICP. METHODS. This prospective study included 30 patients (28 women, two men; median age, 29.9 years) with IIH and papilledema and 21 control individuals (21 women, 0 men; median age, 29.1 years), recruited from January 2017 to July 2019. All participants underwent 3-T brain MRI with MRE; patients with IIH underwent additional MRI examinations with MRE after acute intervention (lumbar puncture with normal closing pressure; n = 11) and/or chronic intervention (medical management or venous sinus stenting with resolution or substantial reduction in papilledema; n = 12). Pituitary height was measured on sagittal MP-RAGE images. Ventricular volumes were estimated using unified segmentation, and postintervention changes were assessed by tensor-based morphometry. Stiffness pattern score and regional stiffness values were estimated from MRE. RESULTS. In patients with IIH, median pituitary height was smaller than in control individuals (3.1 vs 4.9 mm, p < .001) and was increased after chronic (4.0 mm, p = .05), but not acute (2.3 mm, p = .50), intervention. Ventricular volume was not different between patients with IIH and control individuals (p = .33) and did not change after acute (p = .83) or chronic (p = .97) intervention. In patients with IIH, median stiffness pattern score was greater than in control individuals (0.25 vs 0.15, p < .001) and decreased after chronic (0.23, p = .11) but not acute (0.25, p = .49) intervention. Median occipital lobe stiffness was 3.08 kPa in patients with IIH versus 2.94 kPa in control individuals (p = .07) and did not change after acute (3.24 kPa, p = .73) or chronic (3.10 kPa, p = .83) intervention. CONCLUSION. IIH is associated with a small pituitary and increased brain stiffness pattern score; both findings may respond to chronic interventions to lower ICP. CLINICAL IMPACT. The "partially empty sella" sign and brain stiffness pattern score may serve as dynamic markers of ICP in IIH.

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.

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.

Impact of material homogeneity assumption on cortical stiffness estimates by MR elastography

PURPOSE

Inversion algorithms used to convert acquired MR elastography wave data into material property estimates often assume that the underlying materials are locally homogeneous. Here we evaluate the impact of that assumption on stiffness estimates in gray-matter regions of interest in brain MR elastography.

METHODS

We describe an updated neural network inversion framework using finite-difference model-derived data to train convolutional neural network inversion algorithms. Neural network inversions trained on homogeneous simulations (homogeneous learned inversions [HLIs]) or inhomogeneous simulations (inhomogeneous learned inversions [ILIs]) are generated with a variety of kernel sizes. These inversions are evaluated in a brain MR elastography simulation experiment and in vivo in a test-retest repeatability experiment including 10 healthy volunteers.

RESULTS

In simulation and in vivo, HLI and ILI with small kernels produce similar results. As kernel size increases, the assumption of homogeneity has a larger effect, and HLI and ILI stiffness estimates show larger differences. At each inversion's optimal kernel size in simulation (7 × 7 × 7 for HLI, 11 × 11 × 11 for ILI), ILI is more sensitive to true changes in stiffness in gray-matter regions of interest in simulation. In vivo, there is no difference in the region-level repeatability of stiffness estimates between the inversions, although ILI appears to better maintain the stiffness map structure as kernel size increases, while decreasing the spatial variance in stiffness estimates.

CONCLUSIONS

This study suggests that inhomogeneous inversions provide small but significant benefits even when large stiffness gradients are absent.

Magnetic resonance elastography in normal pressure hydrocephalus-a scoping review

BACKGROUND

Magnetic resonance elastography (MRE) of the brain allows quantitative measurement of tissue mechanics. Multiple studies are exploring possible applications in normal pressure hydrocephalus (NPH) in clinical and paraclinical contexts. This is of great interest in neurological surgery due to challenges related to diagnosis and prediction of treatment effects. In this scoping review, we present a topical overview and discuss the current literature, with particular attention to clinical implications and current challenges.

METHODS

The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), the articles were screened for relevance. Thirty articles were subject to detailed screening, and key technical and clinical data items were extracted. The inclusion criteria included the use of MRE on human subjects with NPH.

RESULTS

Seven articles were included in the final study. These studies had various objectives including the role of MRE in the assessment of regional elastic changes in NPH, shunt effect, and evaluation of NPH symptoms. MRE revealed patterns of mechanical changes in NPH that differed from other dementias. Regional MRE changes were associated with specific NPH signs and symptoms. Neurosurgical shunting caused partial normalization in tissue scaffold parameters. The studies were highly heterogeneous in technical aspects and design.

CONCLUSION

MRE studies in NPH are still limited by few participants, variable cohorts, inconsistent methodologies, and technical challenges, but the approach shows great potential for future clinical application.

Correlated noise in brain magnetic resonance elastography

PURPOSE

Magnetic resonance elastography (MRE) uses phase-contrast MRI to generate mechanical property maps of the in vivo brain through imaging of tissue deformation from induced mechanical vibration. The mechanical property estimation process in MRE can be susceptible to noise from physiological and mechanical sources encoded in the phase, which is expected to be highly correlated. This correlated noise has yet to be characterized in brain MRE, and its effects on mechanical property estimates computed using inversion algorithms are undetermined.

METHODS

To characterize the effects of signal noise in MRE, we conducted 3 experiments quantifying (1) physiomechanical sources of signal noise, (2) physiological noise because of cardiac-induced movement, and (3) impact of correlated noise on mechanical property estimates. We use a correlation length metric to estimate the extent that correlated signal persists in MRE images and demonstrate the effect of correlated noise on property estimates through simulations.

RESULTS

We found that both physiological noise and vibration noise were greater than image noise and were spatially correlated across all subjects. Added physiological and vibration noise to simulated data resulted in property maps with higher error than equivalent levels of Gaussian noise.

CONCLUSION

Our work provides the foundation to understand contributors to brain MRE data quality and provides recommendations for future work to correct for signal noise in MRE.

Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks

Artificial neural networks (ANN), established tools in machine learning, are applied to the problem of estimating parameters of a transversely isotropic (TI) material model using data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). We use neural networks to estimate parameters from experimental measurements of ultrasound-induced shear waves after training on analogous data from simulations of a computer model with similar loading, geometry, and boundary conditions. Strain ratios and shear-wave speeds (from MRE) and fiber direction (the direction of maximum diffusivity from diffusion tensor imaging (DTI)) are used as inputs to neural networks trained to estimate the parameters of a TI material (baseline shear modulus μ, shear anisotropy φ, and tensile anisotropy ζ). Ensembles of neural networks are applied to obtain distributions of parameter estimates. The robustness of this approach is assessed by quantifying the sensitivity of property estimates to assumptions in modeling (such as assumed loss factor) and choices in fitting (such as the size of the neural network). This study demonstrates the successful application of simulation-trained neural networks to estimate anisotropic material parameters from complementary MRE and DTI imaging data.

Development of three-dimensional integral-type reconstruction formula for magnetic resonance elastography

PURPOSE

The viscoelasticity (storage modulus and loss modulus) of living tissues is known to be related to diseases. Magnetic resonance elastography (MRE) is a quantitative method for non-invasive measuring viscoelasticity. The viscoelasticity is calculated from the elastic wave images using an inversion algorithm. The estimation accuracy of the inversion algorithm is degraded by background noise. This study aims to propose novel inversion algorithms that are applicable for noisy elastic wave images.

METHODS

The proposed algorithms are based on the Voigt-type viscoelastic equation. The algorithms are designed to improve the noise robustness by avoiding direct differentiation of measurement data by virtue of Green's formula. Similarly, stabilization is introduced to the curl-operator which works to eliminate the compression waves in measurement data. To clarify the characteristics of the algorithms, the proposed algorithms were compared with the conventional algorithms using isotropic and anisotropic voxel numerical simulations and phantom experimental data.

RESULTS

From the results of the numerical simulations, normalized errors of stiffness of proposed algorithms were 3% or less. The proposed algorithms mostly showed better results than the conventional algorithms despite noisy elastic wave images. From the gel phantom experiment, we confirmed the same tendency as the characteristics of the algorithms observed in the numerical simulation results.

CONCLUSION

We have developed a novel inversion algorithm and evaluated it quantitatively. The results confirm that the proposed algorithms are highly quantitative and noise-robust methods for estimating storage and loss modulus regardless of noise, voxel anisotropy, and propagation direction. Therefore, the proposed algorithms will appropriate to various three-dimensional MRE systems.

Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview

This article presents an overview of deep learning (DL) and its applications to function approximation for MR in medicine. The aim of this article is to help readers develop various applications of DL. DL has made a large impact on the literature of many medical sciences, including MR. However, its technical details are not easily understandable for non-experts of machine learning (ML).The first part of this article presents an overview of DL and its related technologies, such as artificial intelligence (AI) and ML. AI is explained as a function that can receive many inputs and produce many outputs. ML is a process of fitting the function to training data. DL is a kind of ML, which uses a composite of many functions to approximate the function of interest. This composite function is called a deep neural network (DNN), and the functions composited into a DNN are called layers. This first part also covers the underlying technologies required for DL, such as loss functions, optimization, initialization, linear layers, non-linearities, normalization, recurrent neural networks, regularization, data augmentation, residual connections, autoencoders, generative adversarial networks, model and data sizes, and complex-valued neural networks.The second part of this article presents an overview of the applications of DL in MR and explains how functions represented as DNNs are applied to various applications, such as RF pulse, pulse sequence, reconstruction, motion correction, spectroscopy, parameter mapping, image synthesis, and segmentation.

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.

Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives

Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.

Aging brain mechanics: Progress and promise of magnetic resonance elastography

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

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.

SNR Weighting for Shear Wave Speed Reconstruction in Tomoelastography

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

MR elastography: Principles, guidelines, and terminology

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

MR elastography of liver: current status and future perspectives

Non-invasive evaluation of liver fibrosis has evolved over the last couple of decades. Currently, elastography techniques are the most widely used non-invasive methods for clinical evaluation of chronic liver disease (CLD). MR elastography (MRE) of the liver has been used in the clinical practice for nearly a decade and continues to be widely accepted for detection and staging of liver fibrosis. With MRE, one can directly visualize propagating shear waves through the liver and an inversion algorithm in the scanner automatically converts the shear wave properties into an elastogram (stiffness map) on which liver stiffness can be calculated. The commonly used MRE method, two-dimensional gradient recalled echo (2D-GRE) sequence has produced excellent results in the evaluation of liver fibrosis in CLD from various etiologies and newer clinical indications continue to emerge. Advances in MRE technique, including 3D MRE, automated liver elasticity calculation, improvements in shear wave delivery and patient experience, are promising to provide a faster and more reliable MRE of liver. Innovations, including evaluation of mechanical parameters, such as loss modulus, displacement, and volumetric strain, are promising for comprehensive evaluation of CLD as well as understanding pathophysiology, and in differentiating various etiologies of CLD. In this review, the current status of the MRE of liver in CLD are outlined and followed by a brief description of advanced techniques and innovations in MRE of liver.

Artificial neural networks for magnetic resonance elastography stiffness estimation in inhomogeneous materials

PURPOSE

To test the hypothesis that removing the assumption of material homogeneity will improve the spatial accuracy of stiffness estimates made by Magnetic Resonance Elastography (MRE).

METHODS

An artificial neural network was trained using synthetic wave data computed using a coupled harmonic oscillator model. Material properties were allowed to vary in a piecewise smooth pattern. This neural network inversion (Inhomogeneous Learned Inversion (ILI)) was compared against a previous homogeneous neural network inversion (Homogeneous Learned Inversion (HLI)) and conventional direct inversion (DI) in simulation, phantom, and in-vivo experiments.

RESULTS

In simulation experiments, ILI was more accurate than HLI and DI in predicting the stiffness of an inclusion in noise-free, low-noise, and high-noise data. In the phantom experiment, ILI delineated inclusions ≤ 2.25 cm in diameter more clearly than HLI and DI, and provided a higher contrast-to-noise ratio for all inclusions. In a series of stiff brain tumors, ILI shows sharper stiffness transitions at the edges of tumors than the other inversions evaluated.

CONCLUSION

ILI is an artificial neural network based framework for MRE inversion that does not assume homogeneity in material stiffness. Preliminary results suggest that it provides more accurate stiffness estimates and better contrast in small inclusions and at large stiffness gradients than existing algorithms that assume local homogeneity. These results support the need for continued exploration of learning-based approaches to MRE inversion, particularly for applications where high resolution is required.

Identification of Normal Pressure Hydrocephalus by Disease-Specific Patterns of Brain Stiffness and Damping Ratio

OBJECTIVES

The aim of this study was to perform a whole-brain analysis of alterations in brain mechanical properties due to normal pressure hydrocephalus (NPH).

MATERIALS AND METHODS

Magnetic resonance elastography (MRE) examinations were performed on 85 participants, including 44 cognitively unimpaired controls, 33 with NPH, and 8 who were amyloid-positive with Alzheimer clinical syndrome. A custom neural network inversion was used to estimate stiffness and damping ratio from patches of displacement data, accounting for edges by training the network to estimate the mechanical properties in the presence of missing data. This learned inversion was first compared with a standard analytical approach in simulation experiments and then applied to the in vivo MRE measurements. The effect of NPH on the mechanical properties was then assessed by voxel-wise modeling of the stiffness and damping ratio maps. Finally, a pattern analysis was performed on each individual's mechanical property maps by computing the correlation between each person's maps with the expected NPH effect. These features were used to fit a classifier and assess diagnostic accuracy.

RESULTS

The voxel-wise analysis of the in vivo mechanical property maps revealed a unique pattern in participants with NPH, including a concentric pattern of stiffening near the dural surface and softening near the ventricles, as well as decreased damping ratio predominantly in superior regions of the white matter (family-wise error corrected P < 0.05 at cluster level). The pattern of viscoelastic changes in each participant predicted NPH status in this cohort, separating participants with NPH from the control and the amyloid-positive with Alzheimer clinical syndrome groups, with areas under the receiver operating characteristic curve of 0.999 and 1, respectively.

CONCLUSIONS

This study provides motivation for further development of the neural network inversion framework and demonstrates the potential of MRE as a novel tool to diagnose NPH and provide a window into its pathogenesis.

Liver fibrosis imaging: A clinical review of ultrasound and magnetic resonance elastography

Liver fibrosis is a histological hallmark of most chronic liver diseases, which can progress to cirrhosis and liver failure, and predisposes to hepatocellular carcinoma. Accurate diagnosis of liver fibrosis is necessary for prognosis, risk stratification, and treatment decision-making. Liver biopsy, the reference standard for assessing liver fibrosis, is invasive, costly, and impractical for surveillance and treatment response monitoring. Elastography offers a noninvasive, objective, and quantitative alternative to liver biopsy. This article discusses the need for noninvasive assessment of liver fibrosis and reviews the comparative advantages and limitations of ultrasound and magnetic resonance elastography techniques with respect to their basic concepts, acquisition, processing, and diagnostic performance. Variations in clinical contexts of use and common pitfalls associated with each technique are considered. In addition, current challenges and future directions to improve the diagnostic accuracy and clinical utility of elastography techniques are discussed. Level of Evidence: 5 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:25-42.

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.

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data

OBJECTIVE. The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinical and nonelastographic MRI radiomic features in pediatric and young adult patients with known or suspected liver disease. MATERIALS AND METHODS. Clinical data (27 demographic, anthropomorphic, medical history, and laboratory features), MRI presence of liver fat and chemical shift-encoded fat fraction, and MRE mean liver stiffness measurements were retrieved from electronic medical records. MRI radiomic data (105 features) were extracted from T2-weighted fast spin-echo images. Patients were categorized by mean liver stiffness (< 3 vs ≥ 3 kPa). Support vector machine (SVM) models were used to perform two-class classification using clinical features, radiomic features, and both clinical and radiomic features. Our proposed model was internally evaluated in 225 patients (mean age, 14.1 years) and externally evaluated in an independent cohort of 84 patients (mean age, 13.7 years). Diagnostic performance was assessed using ROC AUC values. RESULTS. In our internal cross-validation model, the combination of clinical and radiomic features produced the best performance (AUC = 0.84), compared with clinical (AUC = 0.77) or radiomic (AUC = 0.70) features alone. Using both clinical and radiomic features, the SVM model was able to correctly classify patients with accuracy of 81.8%, sensitivity of 72.2%, and specificity of 87.0%. In our external validation experiment, this SVM model achieved an accuracy of 75.0%, sensitivity of 63.6%, specificity of 82.4%, and AUC of 0.80. CONCLUSION. An SVM learning model incorporating clinical and T2-weighted radiomic features has fair-to-good diagnostic performance for categorically classifying liver stiffness.

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.

Altered brain tissue viscoelasticity in pediatric cerebral palsy measured by magnetic resonance elastography

Cerebral palsy (CP) is a neurodevelopmental disorder that results in functional motor impairment and disability in children. CP is characterized by neural injury though many children do not exhibit brain lesions or damage. Advanced structural MRI measures may be more sensitively related to clinical outcomes in this population. Magnetic resonance elastography (MRE) measures the viscoelastic mechanical properties of brain tissue, which vary extensively between normal and disease states, and we hypothesized that the viscoelasticity of brain tissue is reduced in children with CP. Using a global region-of-interest-based analysis, we found that the stiffness of the cerebral gray matter in children with CP is significantly lower than in typically developing (TD) children, while the damping ratio of gray matter is significantly higher in CP. A voxel-wise analysis confirmed this finding, and additionally found stiffness and damping ratio differences between groups in regions of white matter. These results indicate that there is a difference in brain tissue health in children with CP that is quantifiable through stiffness and damping ratio measured with MRE. Understanding brain tissue mechanics in the pediatric CP population may aid in the diagnosis and evaluation of CP.

Magnetic Resonance Elastography of Liver: Current Update

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

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.

Advances and Future Direction of Magnetic Resonance Elastography

The mechanical properties of soft tissues are closely associated with a variety of diseases. This motivates the development of elastography techniques in which tissue mechanical properties are quantitatively estimated through imaging. Magnetic resonance elastography (MRE) is a noninvasive phase-contrast MR technique wherein shear modulus of soft tissue can be spatially and temporally estimated. MRE has recently received significant attention due to its capability in noninvasively estimating tissue mechanical properties, which can offer considerable diagnostic potential. In this work, recent technology advances of MRE, its future clinical applications, and the related limitations will be discussed.