Magnetic Resonance Imaging Elastography Research Articles

Individualized driver amplitude in liver MR elastography: a linear regression study.

Current liver magnetic resonance elastography (MRE) scans often require adjustments to driver amplitude to produce acceptable images. This could lead to time wastage and the potential loss of an opportunity to capture a high-quality image. To construct a linear regression model of individualized driver amplitude to improve liver MRE image quality. Data from 95 liver MRE scans of 61 participants, including abdominal missing volume ratio (AMVR), breath-holding status, the distance from the passive driver on the skin surface to the liver edge (Dd-l), body mass index (BMI), and lateral deflection of the passive driver with respect to the human sagittal plane (Angle α), were continuously collected. The Spearman correlation analysis and lasso regression were conducted to screen the independent variables. Multiple linear regression equations were developed to determine the optimal amplitude prediction model. The optimal formula for linear regression models: driver amplitude (%) = -16.80 + 78.59 × AMVR - 11.12 × breath-holding (end of expiration = 1, end of inspiration = 0) + 3.16 × Dd-l + 1.94 × BMI + 0.34 × angle α, with the model passing the F test (F = 22.455, P <0.001) and R2 value of 0.558. The individualized amplitude prediction model based on AMVR, breath-holding status, Dd-l, BMI, and angle α is a valuable tool in liver MRE examination.

Comparison of spin-echo echo planar imaging and gradient-recalled echo sequences in magnetic resonance elastography of liver at 1.5T same MRI scanner.

Magnetic resonance elastography (MRE) is used to measure liver stiffness with gradient-recalled echo (GRE)-based and spin-echo echo planar imaging (SE-EPI)-based sequences. We compared the liver stiffness (LS) values of the two sequences on a 1.5-T MR imaging scanner. This is a retrospective study. An MRE imaging section was obtained from a horizontal section of the liver. Region of interest was drawn on the elastogram, and the mean LS and pixel values were measured and compared. The correlations between proton density fat fraction, R2* values, and biochemical data from electronic medical records were confirmed, and multivariate analysis was performed. The mean LS values were 3.01±1.78kPa for GRE and 3.13±1.57kPa for SE-EPI, showing excellent agreement and a strong correlation between the two sequences (correlation coefficient r=0.96). The mean pixel values were 369.5±142.7 pixels for GRE and 490.1±197.9 pixels for SE-EPI, showing a significant difference by the Wilcoxon rank sum test (p<0.01). There were no LS unmeasurable cases in SE-EPI, but seven (2.5%) were unmeasurable in GRE, and multivariate analysis showed a significant difference with p<0.001 in R2* values (mean, 92.7Hz) for the GRE method. The GRE and SE-EPI methods were comparable for LS measurements in 1.5-T liver MRE, indicating that SE-EPI MRE is more useful because GRE MRE may not measure cases with high R2* values and the region of interest tends to be smaller.

Prevalence, distribution, and hepatic fibrosis burden of the different subtypes of steatotic liver disease in primary care settings.

In relation to the new umbrella terminology for steatotic liver disease (SLD), we aimed to elucidate the prevalence, distribution, and clinical characteristics of the SLD subgroups in the primary care setting. We retrospectively collected data from 2535 individuals who underwent magnetic resonance elastography and MRI proton density fat fraction during health checkups in 5 primary care health promotion clinics. We evaluated the presence of cardiometabolic risk factors according to predefined criteria and divided all the participants according to the new SLD classification. The prevalence of SLD was 39.13% in the total cohort, and 95.77% of the SLD cases had metabolic dysfunction (one or more cardiometabolic risk factors). The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) was 29.51%, with those of metabolic dysfunction and alcohol associated steatotic liver disease (MetALD) and alcohol-associated liver disease (ALD) at 7.89% and 0.39%, respectively. According to the old criteria, the prevalence of NAFLD was 29.11%, and 95.80% of the NAFLD cases fulfilled the new criteria for MASLD. The distribution of SLD subtypes was highest for MASLD, at 75.40%, followed by MetALD at 20.06%, cryptogenic SLD at 3.33%, and ALD at 1.01%. The MetALD group had a significantly higher mean magnetic resonance elastography than the MASLD or ALD group. Almost all the patients with NAFLD met the new criteria for MASLD. The fibrosis burden of the MetALD group was higher than those of the MASLD and ALD groups.

Fusion of Magnetic Resonance Elastography Images With Computed Tomography and Magnetic Resonance Imaging Using the Human Visual System.

Magnetic resonance elastography (MRE) is used to assess the stiffness of the liver to rule out cirrhosis or fibrosis. The image, nevertheless, is regarded as shear-wave imaging and does not depict any anatomical features. Multimodality medical image fusion (MMIF), such as the fusion of MRE with computed tomography (CT) scan or magnetic resonance imaging (MRI), can help doctors optimize the advantages of each imaging technique. As a result, perceptions serve as valid and valuable assessment criteria. The contrast sensitivity function (CSF), which describes the rates of visual contrast sensitivity through the changing of spatial frequencies, is used mathematically to characterize the human visual system (HVS). As a result, we suggest novel methods for fusing images that use discrete wavelets transform (DWT) based on HVS and CSF models. Images from MRI or CT scan were combined with MRE images, and the outcomes were assessed both subjectively and objectively. Visual inspection of merging images was done throughout the qualitative analysis. The CT-MRE fused images in all four datasets were shown to be superior at maintaining bones and spatial resolution, despite the MRI-MRE being better at exhibiting soft tissues and contrast resolution. It is clear from all four datasets that the liver soft tissue in MRI and CT images mixed successfully with the red-colored stiffness distribution seen in MRE images. The proposed approach outperformed DWT, which produced visual artifacts such as signal loss. Quantitative evaluation using mean, standard deviation, and entropy showed that the generated images from the proposed technique performed better than the source images and DWT. Additionally, peak signal-to-noise ratio, mean square error, correlation coefficient, and structural similarity index measure were employed to compare the two fusion approaches, namely, MRI-MRE and CT-MRE. The comparison did not show the superiority of one approach over the other. In conclusion, both subjective and objective evaluation approaches revealed that the combined images contained more information and characteristics. Hence, the proposed method might be a useful procedure to diagnose and localize the stiffness regions on the liver soft tissue by fusion of MRE with MRI or CT.

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.

Mildly Low Serum Sodium Levels in Chronic Liver Disease: At Risk for Sarcopenia and Portal Hypertension.

Hyponatremia and sarcopenia in advanced chronic liver disease (ACLD) are both associated with portal hypertension (PHT) and worseprognosis. This study investigated their interrelationship. This retrospective study analyzed 751 patients with CLD who underwent magnetic resonance elastography (MRE) at Nippon Kokan Hospital (Kawasaki, Japan). Patients were classified and studied in five groups based on serum sodium (Na) levels: <135, 135-136, 137-138, 139-140, and >140 mEq/L. PHT was assessed by thrombocytopenia, varices, and ascites, and magnetic resonance imaging (MRI) data were used to diagnose sarcopenia. The proportions of the five groups were 3/4/13/32/48 (%), and the mean liver stiffness (LS) was 6.6/5.7/4.2/3.2/3.2 (kPa), with significant progressive increases at Na < 139 (p< 0.01). The incidence of all PHT events and sarcopenia also increased at <139 (each p < 0.01). By contrast, the LS thresholds for predicting thrombocytopenia, varices, and ascites increased from 3.5 to 4.7 and 5.1, respectively, and were the same at 3.4 for low Na (<139) and sarcopenia (all p < 0.01). Multivariate analysis of factors associated with low Na identified LS and sarcopenia as independent factors (p < 0.05 both). In the Cox proportional hazards model, low Na was a significant prognostic factor in ACLD (hazard ratio (HR) 5.33, p < 0.01); however, the albumin-bilirubin (ALBI) score (HR 2.49) and sarcopenia (HR 4.03) were extracted in the multivariate analysis(p < 0.05 both). Studies using MRE imaging showed that low Na levels in CLD are associated with worse prognosis, not only due to elevated LS (i.e., PHT)but also the strong association with sarcopenia.

Displacement and Pressure Reconstruction from Magnetic Resonance Elastography Images: Application to an In Silico Brain Model

Magnetic resonance elastography is a motion-sensitive image modality that allows to measure in vivo tissue displacement fields in response to mechanical excitations. This paper investigates a data assimilation approach for reconstructing tissue displacement and pressure fields in an in silico brain model from partial elastography data. The data assimilation is based on a parametrized-background data-weak methodology, in which the state of the physical system—tissue displacements and pressure fields—is reconstructed from the available data assuming an underlying poroelastic biomechanics model. For this purpose, a physics-informed manifold is built by sampling the space of parameters describing the tissue model close to their physiological ranges to simulate the corresponding poroelastic problem and computing a reduced basis via proper orthogonal decomposition. Displacements and pressure reconstruction are sought in a reduced space after solving a minimization problem that encompasses both the structure of the reduced-order model and the available measurements. The proposed pipeline is validated using synthetic data obtained after simulating the poroelastic mechanics of a physiological brain. The numerical experiments demonstrate that the framework can exhibit accurate joint reconstructions of both displacement and pressure fields. The methodology can be formulated for an arbitrary resolution of available displacement data from pertinent images. It can also inherently handle uncertainty on the physical parameters of the mechanical model by enlarging the physics-informed manifold accordingly. Moreover, the framework can be used to characterize, in silico, biomarkers for pathological conditions by appropriately training the reduced-order model. A first application for the noninvasive estimation of ventricular pressure as an indicator of abnormal intracranial pressure is shown in this contribution.

A diagnostic test of three-dimensional magnetic resonance elastography imaging for preoperative prediction of microvascular invasion in patients with T1 stage clear cell renal carcinoma.

Detection of microvascular invasion (MVI) of kidney tumors is important for selecting the optimal therapeutic strategy. Currently, the prediction of MVI lacks an accurate imaging biomarker. This study evaluated the performance of three-dimensional (3D) magnetic resonance elastography (MRE) imaging in predicting microvascular invasion (MVI) of T1 stage clear cell renal carcinoma (ccRCC). In this prospective study, we conducted pre-surgical imaging with a clinical 3.0 T magnetic resonance imaging (MRI) system. Firstly, 83 consecutive patients were enrolled in this study. A 3D MRE stiffness map was generated and transferred to a post-processing workstation. Contrast-enhanced computed tomography (CT) was conducted to calculate the tumor enhancement ratio. The presence of MVI was evaluated by histopathological analysis and graded according to the risk stratification based upon the number and distribution. The mean stiffness and CT tumor enhancement ratio was calculated for tumors with or without MVI. The diagnostic performance [sensitivity, specificity, positive predictive value, negative predictive value, area under the curve (AUC)] and independent predicting factors for MVI were investigated. Finally, A total of 80 patients (aged 46.7±13.2 years) were enrolled, including 22 cases of tumors with MVI. The mean MRE stiffness of kidney parenchyma and kidney tumors was 4.8±0.2 and 4.5±0.7 kPa, respectively. There was significant difference in the mean MRE stiffness between tumors with MVI (5.4±0.6 kPa) and tumors without MVI (4.1±0.3 kPa) (P<0.05). The sensitivity, specificity, positive predictive value, negative predictive value, and the AUC for mean stiffness in the prediction of MVI were 100%, 75%, 63%, 96%, and 0.87 [95% confidence interval (CI): 0.72, 0.94], respectively. The corresponding values for the CT tumor enhancement ratio were 90%, 80%, 63%, 96%, and 0.88 (95% CI: 0.71, 0.93), respectively. The odds ratio (OR) value for MRE tumor stiffness and CT kidney tumor enhancement ratio in the prediction of MVI was 2.9 (95% CI: 1.8, 3.7) and 1.2 (95% CI: 1.0, 1.7), respectively (P>0.05). 3D MRE imaging has promising diagnostic performance for predicting MVI in T1 stage ccRCC, which may improve the reliability of surgical strategy selection with T1 stage ccRCC.

Magnetic resonance elastography of the brain: Recent advances

Magnetic resonance elastography (MRE) is an imaging technique for generating maps of quantitative tissue mechanical properties. Mechanical properties of the brain offer sensitive new biomarkers for diagnosing neurological conditions and individual differences in brain health that support cognitive functioning. However, the mechanical complexity of the brain—e.g., heterogeneity in space and scale, frequency- and direction-dependent behavior, skull encasement—require advanced MRE techniques to reliably and accurately map brain tissue properties. We will discuss recent developments in MRE imaging and inversion methods and experiments aimed at more completely estimating brain properties. This includes approaches for determining mechanical anisotropy in white matter tracts comprising aligned axonal fiber bundles from rich acoustic wavefields. And new experiments capturing time-dependent variation in properties and the relationships to cerebral blood flow. Such advances increase the capabilities of MRE as an imaging tool and concomitantly enrich our understanding of the physiological bases of in vivo brain mechanical properties.

MR elastography identifies regions of extracellular matrix reorganization associated with shorter survival in glioblastoma patients.

Biomechanical tissue properties of glioblastoma tumors are heterogeneous, but the molecular mechanisms involved and the biological implications are poorly understood. Here, we combine magnetic resonance elastography (MRE) measurement of tissue stiffness with RNA sequencing of tissue biopsies to explore the molecular characteristics of the stiffness signal. MRE was performed preoperatively in 13 patients with glioblastoma. Navigated biopsies were harvested during surgery and classified as "stiff" or "soft" according to MRE stiffness measurements (|G*|norm). Twenty-two biopsies from eight patients were analyzed by RNA sequencing. The mean whole-tumor stiffness was lower than normal-appearing white matter. The surgeon's stiffness evaluation did not correlate with the MRE measurements, which suggests that these measures assess different physiological properties. Pathway analysis of the differentially expressed genes between "stiff" and "soft" biopsies showed that genes involved in extracellular matrix reorganization and cellular adhesion were overexpressed in "stiff" biopsies. Supervised dimensionality reduction identified a gene expression signal separating "stiff" and "soft" biopsies. Using the NIH Genomic Data Portal, 265 glioblastoma patients were divided into those with (n = 63) and without (n = 202) this gene expression signal. The median survival time of patients with tumors expressing the gene signal associated with "stiff" biopsies was 100 days shorter than that of patients not expressing it (360 versus 460 days, hazard ratio: 1.45, P < .05). MRE imaging of glioblastoma can provide noninvasive information on intratumoral heterogeneity. Regions of increased stiffness were associated with extracellular matrix reorganization. An expression signal associated with "stiff" biopsies correlated with shorter survival of glioblastoma patients.

Mapping brain mechanical property maturation from childhood to adulthood

Magnetic resonance elastography (MRE) is a phase contrast MRI technique which uses external palpation to create maps of brain mechanical properties noninvasively and in vivo. These mechanical properties are sensitive to tissue microstructure and reflect tissue integrity. MRE has been used extensively to study aging and neurodegeneration, and to assess individual cognitive differences in adults, but little is known about mechanical properties of the pediatric brain. Here we use high-resolution MRE imaging in participants of ages ranging from childhood to adulthood to understand brain mechanical properties across brain maturation. We find that brain mechanical properties differ considerably between childhood and adulthood, and that neuroanatomical subregions have differing maturational trajectories. Overall, we observe lower brain stiffness and greater brain damping ratio with increasing age from 5 to 35 years. Gray and white matter change differently during maturation, with larger changes occurring in gray matter for both stiffness and damping ratio. We also found that subregions of cortical and subcortical gray matter change differently, with the caudate and thalamus changing the most with age in both stiffness and damping ratio, while cortical subregions have different relationships with age, even between neighboring regions. Understanding how brain mechanical properties mature using high-resolution MRE will allow for a deeper understanding of the neural substrates supporting brain function at this age and can inform future studies of atypical maturation.

Mechanical Properties of the Developing Brain are Associated with Language Input and Vocabulary Outcome

ABSTRACT The quality of language that children hear in their environment is associated with the development of language-related brain regions, in turn promoting vocabulary knowledge. Although informative, it remains unknown how these environmental influences alter the structure of neural tissue and subsequent vocabulary outcomes. The current study uses magnetic resonance elastography (MRE) to examine how children’s language environments underlie brain tissue mechanical properties, characterized as brain tissue stiffness and damping ratio, and promote vocabulary knowledge. Twenty-five children, ages 5–7, had their audio and video recorded while engaging in a play session with their parents. Children also completed the Picture Vocabulary Task (from NIH Toolbox) and participated in an MRI, where MRE and anatomical images were acquired. Higher quality input was associated with greater stiffness in the bilateral inferior frontal gyrus and right superior temporal gyrus, whereas greater vocabulary knowledge was associated with lower damping ratio in the right inferior frontal gyrus. These findings suggest changes in neural tissue composition are sensitive to malleable aspects of the environment, whereas tissue organization is more strongly associated with vocabulary outcome. Notably, these associations were independent of maternal education, suggesting more proximal measures of a child’s environment may be the source of differences in neural tissue structure underlying variability in vocabulary outcomes.

1424-P: Impact of Laparoscopic Sleeve Gastrectomy on Nonalcoholic Fatty Liver Disease in Obese Patients with Type 2 Diabetes: Prospective Observational Study

Background: Bariatric/metabolic surgery is a valid treatment option for patients with obesity, type 2 diabetes (T2DM) and nonalcoholic fatty liver disease (NAFLD) . Although magnetic resonance imaging (MRI) is more accurate, the evaluation of NAFLD is generally limited to ultrasonography and various scoring systems. Here, we show a therapeutic effect of laparoscopic sleeve gastrectomy (LSG) on NAFLD in Japanese patients with T2DM using MRI. Methods: We conducted a prospective observational study of Japanese patients with obesity, T2DM, and NAFLD over 1 year (UMIN000034667) . The participants were 20 surgically (LSG group) and 20 medically (control group) treated patients. We compared various parameters at baseline and 1 year later in the two groups. The primary outcome was the change in the MRI proton density fat fraction and elasticity of the liver. The secondary outcomes were changes in metabolic parameters, including body mass index (BMI) , HbA1c, and diabetic remission rate. Results: To date, 28 patients (LSG 15, control 13) have completed the study. Their baseline BMIs were LSG 39.5 [35.5, 45.1] kg/m2 and control 35.9 [32.6, 43.6] kg/m2 (P=0.12) . One year after surgery, the index of hepatic steatosis, MRI proton density fat fraction (LSG −13.3±8.4%, control −0.8±5.5%, P&amp;lt;0.01) , was significantly lower in the LSG group. A second index, stiffness on MRI elastography (LSG −0.2 [−0.3, 0.1] kPa, control −0.[−0.35, 0.4] kPa, P=0.60) tended to be lower. In addition, BMI (LSG −9.5±3.2 kg/m2, control −0.1±0.6 kg/m2; P&amp;lt;0.01) and HbA1c (LSG −1.4±1.0%, control 0.3±0.8 kg/m2; P&amp;lt;0.01) were significantly lower. The postoperative diabetic remission rate was 86.7%. Conclusion: We have shown that LSG causes marked weight loss and diabetic remission in many patients. NAFLD was also markedly ameliorated, especially hepatic steatosis, although a longer study may be required to detect an improvement in fibrosis. Disclosure Y.Oe: None. T.Atsumi: Consultant; AbbVie Inc., AstraZeneca, MEDICAL &amp; BIOLOGICAL LABORATORIES CO., Nippon Boehringer Ingelheim Co. Ltd., Novartis Pharma K.K., Ono Pharmaceutical Co., Ltd., Pfizer Inc., Research Support; AbbVie Inc., Alexion Pharmaceuticals, Inc., Astellas Pharma Inc., Chugai Pharmaceutical Co., Ltd., Daiichi Sankyo, Mitsubishi Tanabe Pharma Corporation, Nippon Boehringer Ingelheim Co. Ltd., Otsuka Pharmaceutical Co., Ltd., Pfizer Inc., Taiho Pharmaceutical Co. Ltd., Takeda Pharmaceutical Company Limited, Teijin Pharma Limited, Speaker's Bureau; AbbVie Inc., Astellas Pharma Inc., Bristol-Myers Squibb Company, Chugai Pharmaceutical Co., Ltd., Daiichi Sankyo, Eisai Co., Ltd., Eli Lilly and Company, Kyowa Kirin Co., Ltd., Mitsubishi Tanabe Pharma Corporation, Novartis Pharma K.K., Pfizer Inc., Taiho Pharmaceutical Co. Ltd., Takeda Pharmaceutical Company Limited, UCB, Inc. H.Miyoshi: Research Support; Abbott Japan Co., Ltd., Boehringer Ingelheim International GmbH, Daiichi Sankyo, Kowa Company, Ltd., LifeScan, Mitsubishi Tanabe Pharma Corporation, Novo Nordisk, Ono Pharmaceutical Co., Ltd., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Holdings Co., Ltd., Speaker's Bureau; Astellas Pharma Inc., Boehringer Ingelheim International GmbH, Eli Lilly and Company, Kowa Company, Ltd., Merck &amp; Co., Inc., Mitsubishi Tanabe Pharma Corporation, Novo Nordisk, Ono Pharmaceutical Co., Ltd., Sanofi K.K., Sumitomo Dainippon Pharma Co., Ltd., Taisho Pharmaceutical Holdings Co., Ltd. T.Takase: None. K.Cho: None. K.Ogawa: None. Y.Ebihara: None. A.Miya: None. H.Nomoto: None. H.Kameda: None. A.Nakamura: Research Support; Kissei Pharmaceutical Co., Ltd., MSD, Nippon Boehringer Ingelheim, Taisho Pharmaceutical Holdings Co., Ltd.

Magnetic Resonance Elastography-derived Stiffness Predicts Renal Function Loss and Is Associated With Microvascular Inflammation in Kidney Transplant Recipients.

Background.Organ stiffening can be caused by inflammation and fibrosis, processes that are common causes of transplant kidney dysfunction. Magnetic resonance elastography (MRE) is a contrast-free, noninvasive imaging modality that measures kidney stiffness. The objective of this study was to assess the ability of MRE to serve as a prognostic factor for renal outcomes.Methods.Patients were recruited from the St Michael’s Hospital Kidney Transplant Clinic. Relevant baseline demographic, clinical, and Banff histologic information, along with follow-up estimated glomerular filtration rate (eGFR) data, were recorded. Two-dimensional gradient-echo MRE imaging was performed to obtain kidney “stiffness” maps. Binary logistic regression analyses were performed to examine for relationships between stiffness and microvascular inflammation score. Linear mixed-effects modeling was used to assess the relationship between stiffness and eGFR change over time controlling for other baseline variables. A G2-likelihood ratio Chi-squared test was performed to compare between the baseline models with and without “stiffness.”Results.Sixty-eight transplant kidneys were scanned in 66 patients (mean age 56 ± 12 y, 24 females), with 38 allografts undergoing a contemporaneous biopsy. Mean transplant vintage was 7.0 ± 6.8 y. In biopsied allografts, MRE-derived allograft stiffness was associated only with microvascular inflammation (Banff g + ptc score, Spearman ρ = 0.43, P = 0.01), but no other histologic parameters. Stiffness was negatively associated with eGFR change over time (Stiffness × Time interaction β = –0.80, P < 0.0001), a finding that remained significant even when adjusted for biopsy status and baseline variables (Stiffness × Time interaction β = –0.46, P = 0.04). Conversely, the clinical models including “stiffness” showed significantly better fit (P = 0.04) compared with the baseline clinical models without “stiffness.”Conclusions.MRE-derived renal stiffness provides important prognostic information regarding renal function loss for patients with allograft dysfunction, over and above what is provided by current clinical variables.

Mapping heterogenous anisotropic tissue mechanical properties with transverse isotropic nonlinear inversion MR elastography.

The white matter tracts of brain tissue consist of highly-aligned, myelinated fibers; white matter is structurally anisotropic and is expected to exhibit anisotropic mechanical behavior. In vivo mechanical properties of tissue can be imaged using magnetic resonance elastography (MRE). MRE can detect and monitor natural and disease processes that affect tissue structure; however, most MRE inversion algorithms assume locally homogenous properties and/or isotropic behavior, which can cause artifacts in white matter regions. A heterogeneous, model-based transverse isotropic implementation of a subzone-based nonlinear inversion (TI-NLI) is demonstrated. TI-NLI reconstructs accurate maps of the shear modulus, damping ratio, shear anisotropy, and tensile anisotropy of in vivo brain tissue using standard MRE motion measurements and fiber directions estimated from diffusion tensor imaging (DTI). TI-NLI accuracy was investigated with using synthetic data in both controlled and realistic settings: excellent quantitative and spatial accuracy was observed and cross-talk between estimated parameters was minimal. Ten repeated, in vivo, MRE scans acquired from a healthy subject were co-registered to demonstrate repeatability of the technique. Good resolution of anatomical structures and bilateral symmetry were evident in MRE images of all mechanical property types. Repeatability was similar to isotropic MRE methods and well within the limits required for clinical success. TI-NLI MRE is a promising new technique for clinical research into anisotropic tissues such as the brain and muscle.

Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease

The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis. The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively). Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.

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.

Correlated noise in brain magnetic resonance elastography.

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. 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. 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. 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.

Magnetic resonance elastography of the prostate in patients with lower urinary tract symptoms: feasibility of the modified driver at high multi-frequencies.

To demonstrate the feasibility and diagnostic value of high-frequency magnetic resonance elastography (MRE) for evaluation of prostatic disease in patients with lower urinary tract symptoms (LUTS). 41 patients who underwent preoperative prostate MRI and MRE with a modified driver were enrolled retrospectively from May 2016 to September 2021. All were included in the assessment of MRE image quality, using a qualitative visual inspection and a quantitative confidence map. 35 patients (prostate cancer (PCa), n = 13; non-PCa, n = 22) undergoing prostatectomy or biopsy were evaluated for the diagnostic performance of stiffness values. The confidence values and the stiffness values were analyzed by one-way analysis of variance (ANOVA) and independent samples T test, respectively. Area under the receiver operating characteristic (AUROC) analysis was performed. Through the qualitative analysis, all MRE acquisitions were successful at 60, 90, 120 and 150Hz. The quantitative confidence values were significantly lower at 60Hz (0.683 ± 0.055) and 90Hz (0.762 ± 0.048) than that at 120Hz (0.814 ± 0.049) and 150Hz (0.840 ± 0.049), all P < 0.001. The stiffness of PCa was higher than non-PCa at 90Hz (P = 0.008), 120Hz (P < 0.001) and 150Hz (P < 0.001). The AUCs were 0.773, 0.881 and 0.944, respectively. Prostate MRE using the modified driver is feasible at 60-150Hz and image quality is better at higher frequencies. Prostate MRE may be useful and helpful to evaluate prostate diseases in patients with LUTS at higher frequencies; however, further study may be warranted with larger population in future.

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

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