Preoperative assessment of meningioma consistency using a combination of magnetic resonance elastography and diffusion tensor imaging.

BACKGROUND AND PURPOSE: Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a non-invasive prediction model for meningioma consistency utilizing magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). MATERIALS AND METHODS: Ninety-four patients (52yr ± 22, 69 females, 25 males) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DTI, and MRE. Combined MRE-DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated using receiver operating characteristic (ROC) curve. Further validation was conducted in twenty-seven stereotactic biopsies using indentation tests and underlying mechanism was investigated by histologic analysis. RESULTS: Among all the imaging modalities, MRE demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.70-0.93). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.78-0.97), surpassing any modality alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MRE-DTI model was suitable for predicting intra-tumor consistency. Histological analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content. CONCLUSIONS: The MRE-DTI combined model is effective in noninvasive prediction of meningioma consistency. ABBREVIATIONS: MRE = magnetic resonance elastography; FA = fractional anisotropy; ROC = receiver operating characteristic; AUC = area under curve.

Indirect Shear Wave Excitation for Brain Magnetic Resonance Elastography with Minimal Cerebral Blood Flow Alteration.

Magnetic resonance elastography (MRE) of brain relies on inducing and measuring shear waves in the brain. However, studies have shown vibration could induce changes in cerebral blood flow (CBF), which has a modulation effect and can affect the biomechanical properties measured. OBJECTIVE: This work demonstrates the initial prototype of the indirect excitation method, which can generate shear waves in the brain with minimal changes in CBF. METHODS: A simple system was designed to produce stable vibrations underneath the neck. Instead of directly stimulating the skull, shear waves were indirectly transmitted to the brain through the spine and brainstem. RESULTS: Phantom results showed that the proposed actuator did not interfere with the routine imaging sequence and successfully generated multifrequency shear waves. When compared with the conventional direct head stimulation method, brain MRE results from the proposed actuator showed no significant differences in terms of intraclass correlation coefficients (ICC) and coefficients of variation (CV). Moreover, the octahedral shear strain (OSS) generated by the indirect excitation in the frontal and parietal lobes decreased by 25.96% and 16.73% respectively. Evaluation of CBF in healthy volunteers revealed no significant changes for the indirect excitation method, whereas significant decreases in CBF were observed in four subregions when employing direct excitation. CONCLUSION: The proposed actuator offers a more accurate and comfortable approach to MRE measurements while causing minimal CBF alterations. SIGNIFICANCE: This work presents the first demonstration of an indirect excitation brain MRE system that minimizes CBF changes, thus holding potential for future applications of brain MRE.

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.

Three-dimensional magnetic resonance elastography combining proton-density fat fraction precisely identifies metabolic dysfunction-associated steatohepatitis with significant fibrosis.

PURPOSE: Patients with metabolic dysfunction-associated steatohepatitis (MASH) and significant fibrosis (fibrosis stage≥2), known as Fibro-MASH, are at increased risk of liver-related outcomes and lower rates of spontaneous disease regression. The aim was to investigate three-dimensional MR elastography (3D-MRE) combining proton-density fat fraction (PDFF) as a means of identifying Fibro-MASH. METHODS: Forty-eight New Zealand rabbits were fed a high-fat/cholesterol or standard diet to obtain different disease activity and fibrosis stages. Shear stiffness (SS) and Damping Ratio (DR) were derived from 3D-MRE, whereas PDFF was from a volumetric 3D imaging sequence. Steatosis grade, metabolic dysfunction-associated steatotic liver disease activity score (MAS), and fibrosis stage were diagnosed histologically. Serum markers of fibrosis and inflammation were also measured. Correlation and comparison analysis, Receiver operating characteristic curves (ROC), Delong test, logistic regression analysis, and Net reclassification improvement (NRI) were performed. RESULTS: PDFF correlated with steatosis grade (rho = 0.853). SS increased with developed liver fibrosis (rho = 0.837). DR correlated with MAS grade (rho = 0.678). The areas under the ROC (AUROCs) of SS for fibrosis grading were 0.961 and 0.953 for ≥F2, and ≥ F3, respectively. All the biochemical parameters were considered but excluded from the logistic regression analysis to identify Fibro-MASH. FF, SS, and DR were finally included in the further analysis. The three-parameter model combining PDFF, SS, and DR showed significant improvement in NRI over the model combining SS and PDFF (AUROC 0.973 vs. 0.906, P = 0.081; NRI 0.28, P < 0.05). CONCLUSION: 3D-MRE combining PDFF may characterize the state of fat content, disease activity and fibrosis, thus precisely identify Fibro-MASH.

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.

Assessment of vibration modulated regional cerebral blood flow with MRI.

Human brain experiences vibration of certain magnitude and frequency during various physical activities such as vehicle transportation and machine operation, which may cause traumatic brain injury or other brain diseases. However, the mechanisms of brain pathogenesis due to vibration are not fully elucidated due to the lack of techniques to study brain functions while applying vibration to the brain at a specific magnitude and frequency. Here, this study reported a custom-built head-worn electromagnetic actuator that applied vibration to the brain in vivo at an accurate frequency inside a magnetic resonance imaging scanner while cerebral blood flow (CBF) was acquired. Using this technique, CBF values from 45 healthy volunteers were quantitatively measured immediately following vibration at 20, 30, 40 Hz, respectively. Results showed increasingly reduced CBF with increasing frequency at multiple regions of the brain, while the size of the regions expanded. Importantly, the vibration-induced CBF reduction regions largely fell inside the brain's default mode network (DMN), with about 58 or 46% overlap at 30 or 40 Hz, respectively. These findings demonstrate that vibration as a mechanical stimulus can change strain conditions, which may induce CBF reduction in the brain with regional differences in a frequency-dependent manner. Furthermore, the overlap between vibration-induced CBF reduction regions and DMN suggested a potential relationship between external mechanical stimuli and cognitive functions.

Viscoelastic Characterization of Soft Tissue-Mimicking Gelatin Phantoms using Indentation and Magnetic Resonance Elastography.

Characterization of biomechanical properties of soft biological tissues is important to understand the tissue mechanics and explore the biomechanics-related mechanisms of disease, injury, and development. The mechanical testing method is the most straightforward way for tissue characterization and is considered as verification for in vivo measurement. Among the many ex vivo mechanical testing techniques, the indentation test provides a reliable way, especially for samples that are small, hard to fix, and viscoelastic such as brain tissue. Magnetic resonance elastography (MRE) is a clinically used method to measure the biomechanical properties of soft tissues. Based on shear wave propagation in soft tissues recorded using MRE, viscoelastic properties of soft tissues can be estimated in vivo based on wave equation. Here, the viscoelastic properties of gelatin phantoms with two different concentrations were measured by MRE and indentation. The protocols of phantom fabrication, testing, and modulus estimation have been presented.

Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction.

OBJECTIVE: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be suitable for real-time i-MRI. Therefore, an algorithm to reconstruct images without a temporal pattern as in dynamic imaging is needed for i-MRI. METHODS: We proposed a low-rank and sparsity (LS) decomposition algorithm with framelet transform to reconstruct the interventional feature with a high temporal resolution. Different from the existing LS-based algorithms, the spatial sparsity of both the low-rank and sparsity components was used. We also used a primal dual fixed point (PDFP) method for optimization of the objective function to avoid solving sub-problems. Intervention experiments with gelatin and brain phantoms were carried out for validation. RESULTS: The LS decomposition with framelet transform and PDFP could provide the best reconstruction performance compared with those without. Satisfying reconstruction results were obtained with only 10 radial spokes for a temporal resolution of 60 ms. CONCLUSION AND SIGNIFICANCE: The proposed method has the potential for i-MRI in many different application scenarios.

Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction.

Objective. To achieve fast magnetic resonance elastography (MRE) at a low frequency for better shear modulus estimation of the brain.Approach. We proposed a multiphase radial DENSE MRE (MRD-MRE) sequence and an improved GRASP algorithm utilizing the sparsity of the harmonic motion (SH-GRASP) for fast MRE at 20 Hz. For the MRD-MRE sequence, the initial position encoded by spatial modulation of magnetization (SPAMM) was decoded by an arbitrary number of readout blocks without increasing the number of phase offsets. Based on the harmonic motion, a modified total variation and temporal Fourier transform were introduced to utilize the sparsity in the temporal domain. Both phantom and brain experiments were carried out and compared with that from multiphase Cartesian DENSE-MRE (MCD-MRE), and conventional gradient echo sequence (GRE-MRE). Reconstruction performance was also compared with GRASP and compressed sensing.Main results. Results showed the scanning time of a fully sampled image with four phase offsets for MRD-MRE was only 1/5 of that from GRE-MRE. The wave patterns and estimated stiffness maps were similar to those from MCD-MRE and GRE-MRE. With SH-GRASP, the total scan time could be shortened by additional 4 folds, achieving a total acceleration factor of 20. Better metric values were also obtained using SH-GRASP for reconstruction compared with other algorithms.Significance. The MRD-MRE sequence and SH-GRASP algorithm can be used either in combination or independently to accelerate MRE, showing the potentials for imaging the brain as well as other organs.

A feature-based convolutional neural network for reconstruction of interventional MRI.

Real-time interventional MRI (I-MRI) could help to visualize the position of the interventional feature, thus improving patient outcomes in MR-guided neurosurgery. In particular, in deep brain stimulation, real-time visualization of the intervention procedure using I-MRI could improve the accuracy of the electrode placement. However, the requirements of a high undersampling rate and fast reconstruction speed for real-time imaging pose a great challenge for reconstruction of the interventional images. Based on recent advances in deep learning (DL), we proposed a feature-based convolutional neural network (FbCNN) for reconstructing interventional images from golden-angle radially sampled data. The method was composed of two stages: (a) reconstruction of the interventional feature and (b) feature refinement and postprocessing. With only five radially sampled spokes, the interventional feature was reconstructed with a cascade CNN. The final interventional image was constructed with a refined feature and a fully sampled reference image. With a comparison of traditional reconstruction techniques and recent DL-based methods, it was shown that only FbCNN could reconstruct the interventional feature and the final interventional image. With a reconstruction time of ~ 500 ms per frame and an acceleration factor of ~ 80, it was demonstrated that FbCNN had the potential for application in real-time I-MRI.

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

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

Viscoelastic characterization of injured brain tissue after controlled cortical impact (CCI) using a mouse model.

BACKGROUND: Mechanical properties of the brain tissue are crucial to understand the mechanisms of traumatic brain injury (TBI). Injured brain tissue could induce changes of mechanical properties and anatomical structures. However, limited data is available for the injured tissue. NEW METHOD: We developed a custom-built device to introduce controlled cortical impact (CCI) to brain with controlled impact velocity and direction. A study protocol for measuring the viscoelastic properties of injured brain tissue was also developed. Micro-scale morphological changes of the vasculature were quantified by analyzing confocal images of the brain tissue using CLARITY method. RESULTS: Results showed significant differences of the instantaneous shear modulus of the impact region from different impact angles. However, no significant differences were found for long-term shear modulus by varying the impact angles and velocities. Analysis of the vasculature showed an increased radius of the vessels in the injured tissue compared with that in the control group. COMPARISON WITH EXISTING METHODS: A combination of three different impact velocities and three different impact angles were adopted for producing injury to the brain. In addition, viscoelastic properties were compared between the injured and non-injured regions. The corresponding morphological changes of the vasculature system were also investigated. CONCLUSIONS: The instantaneous shear modulus at the impact region was significantly different for the three impact angles. Compared to that of the control group, increased radius of the vasculature was also observed in the injured brain tissue. Results indicated that the biomechanical and structural changes of the injured tissue were closely related to the impact angles and velocities. Viscoelastic measurements could also help validation of computational models.

Regional biomechanical imaging of liver cancer cells.

Liver cancer is one of the leading cancers, especially in developing countries. Understanding the biomechanical properties of the liver cancer cells can not only help to elucidate the mechanisms behind the cancer progression, but also provide important information for diagnosis and treatment. At the cellular level, we used well-established atomic force microscopy (AFM) techniques to characterize the heterogeneity of mechanical properties of two different types of human liver cancer cells and a normal liver cell line. Stiffness maps with a resolution of 128x128 were acquired for each cell. The distributions of the indentation moduli of the cells showed significant differences between cancerous cells and healthy controls. Significantly, the variability was even greater amongst different types of cancerous cells. Fitting of the histogram of the effective moduli using a normal distribution function showed the Bel7402 cells were stiffer than the normal cells while HepG2 cells were softer. Morphological analysis of the cell structures also showed a higher cytoskeleton content among the cancerous cells. Results provided a foundation for applying knowledge of cell stiffness heterogeneity to search for tissue-level, early-stage indicators of liver cancer.

A multi-purpose electromagnetic actuator for magnetic resonance elastography.

An electromagnetic actuator was designed for magnetic resonance elastography (MRE). The actuator is unique in that it is simple, portable, and capable of brain, abdomen, and phantom imagings. A custom-built control unit was used for controlling the vibration frequency and synchronizing the trigger signals. An actuation unit was built and mounted on the specifically designed clamp and holders for different imaging applications. MRE experiments with respect to gel phantoms, brain, and liver showed that the actuator could produce stable and consistent mechanical waves. Estimated shear modulus using local frequency estimate method demonstrated that the measurement results were in line with that from MRE studies using different actuation systems. The relatively easy setup procedure and simple design indicated that the actuator system had the potential to be applied in many different clinical studies.

Characterizing viscoelastic properties of breast cancer tissue in a mouse model using indentation.

Breast cancer is one of the leading cancer forms affecting females worldwide. Characterizing the mechanical properties of breast cancer tissue is important for diagnosis and uncovering the mechanobiology mechanism. Although most of the studies were based on human cancer tissue, an animal model is still describable for preclinical analysis. Using a custom-build indentation device, we measured the viscoelastic properties of breast cancer tissue from 4T1 and SKBR3 cell lines. A total of 7 samples were tested for each cancer tissue using a mouse model. We observed that a viscoelastic model with 2-term Prony series could best describe the ramp and stress relaxation of the tissue. For long-term responses, the SKBR3 tissues were stiffer in the strain levels of 4-10%, while no significant differences were found for the instantaneous elastic modulus. We also found tissues from both cell lines appeared to be strain-independent for the instantaneous elastic modulus and for the long-term elastic modulus in the strain level of 4-10%. In addition, by inspecting the cellular morphological structure of the two tissues, we found that SKBR3 tissues had a larger volume ratio of nuclei and a smaller volume ratio of extracellular matrix (ECM). Compared with prior cellular mechanics studies, our results indicated that ECM could contribute to the stiffening the tissue-level behavior. The viscoelastic characterization of the breast cancer tissue contributed to the scarce animal model data and provided support for the linear viscoelastic model used for in vivo elastography studies. Results also supplied helpful information for modeling of the breast cancer tissue in the tissue and cellular levels.

A longitudinal study of the mechanical properties of injured brain tissue in a mouse model.

Mechanical properties of brain tissue are crucial to understand the mechanism of traumatic brain injury (TBI). Over the past several decades, most of the studies focused on healthy brain tissues, while few of them are about the injured tissues. Therefore, limited knowledge is known about the mechanical properties of the injured brain tissues. In this study, we used an in vivo mouse model with a weight drop device to study injured brain tissues. Around the injury site, mechanical properties of the injured, neighboring, and the corresponding contralateral regions of interest (ROIs) were measured over five temporal points by indentation. Longitudinal and regional comparisons of the mechanical properties revealed that the ROI of the injured tissue had a higher elastic modulus than the contralateral counterpart one-hour post-injury. However, the elastic modulus decreased one-day post-injury and recovered to be close to the contralateral ROI in 7 days. The elastic modulus curves of the injured and the contralateral counterpart ROIs crossed at time points of 12h and 1 day post-injury, where two significant increases of glial fibrillary acidic protein (GFAP) positive cells were observed. Biological staining results indicated that both the astrocytic responses and the morphological structure could affect the mechanical properties of the injured tissue. The observed longitudinal changes of the mechanical properties at the tissue level and the morphological and biological changes at the cellular level provide insights into understanding the mechanism of TBI. Results are also meaningful for applying emerging in vivo diagnostic tools such as magnetic resonance elastography (MRE) in TBI detection.

A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter.

The aligned axonal fiber bundles in white matter make it suitable to be modeled as a transversely isotropic material. Recent experimental studies have shown that a minimal form, nearly incompressible transversely isotropic (MITI) material model, is capable of describing mechanical anisotropy of white matter. Here, we used a finite element (FE) computational approach to demonstrate the significance of the fifth invariant (I5) when modeling the anisotropic behavior of white matter in the large-strain regime. We first implemented and validated the MITI model in an FE simulation framework for large deformations. Next, we applied the model to a plate-hole structural problem to highlight the significance of the invariant I5 by comparing with the standard fiber reinforcement (SFR) model. We also compared the two models by fitting the experiment data of asymmetric indentation, shear test, and uniaxial stretch of white matter. Our results demonstrated the significance of I5 in describing shear deformation/anisotropy, and illustrated the potential of the MITI model to characterize transversely isotropic white matter tissues in the large-strain regime.