Higher-Resolution Magnetic Resonance Elastography in Meningiomas to Determine Intratumoral Consistency

Repeatability of diffusion-based stiffness prediction - A healthy volunteer study

INTRODUCTION

The study investigated the repeatability of brain diffusion-based stiffness prediction (DWIstiff) in healthy volunteers.

METHODS

Thirty-one healthy volunteers were examined with DWIstiff using two different sets of b-values: b200-1500 s/mm2 (DWIstiff, 1500) and b200-1000 s/mm2 (DWIstiff, 1000). Each b-value set was scanned twice per imaging session without repositioning the participants. DWIstiff images were reconstructed from each set. Two observers delineated regions of interest (ROIs) on each DWIstiff image. The repeatability coefficient (RC), coefficient of variation (CV), inter- and intraobserver agreement were calculated.

RESULTS

After excluding three participants due to image artifacts, the study included twenty-eight volunteers (mean age (range)) 37 years (24-62), 10 males, 18 females). For DWIstiff, 1500, the lowest and the highest RCs were in the parietal lobe (0.52) and respectively the brain stem (1.17). The lowest RC for DWIstiff, 1000 was in the frontal lobe (0.42) and the highest in the brain stem (1.58). The CV for whole brain measurements was 3.83 % for DWIstiff, 1500 and 4.93 % for DWIstiff, 1000. The Bland‒Altman (BA) limits of agreement (LoA) for the intraobserver agreement of DWIstiff, 1500 were -0.90 to 1.06 and respectively -0.78 to 0.88 for DWIstiff, 1000. Regarding interobserver agreement, the LoA were -0.85 to 0.94 for DWIstiff, 1500 and -0.61 to 0.66 for DWIstiff, 1000.

CONCLUSION

DWIstiff is a precise technique with some observer dependence. Repeatability is higher for DWIstiff, 1000 s/mm2 than for DWIstiff 1500 s/mm2.

IMPLICATIONS FOR PRACTICE

Our findings suggest that DWIstiff can reliably detect stiffness changes larger than 4.93 % in healthy volunteers. Further studies should investigate whether the repeatability of DWIstiff may be affected by the presence of pathology such as a brain tumor.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BACKGROUND

The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.

METHODS

Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.

RESULTS

DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).

CONCLUSION

Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Preoperative Magnetic Resonance Elastography (MRE) of Skull Base Tumours: A Review

Conventional magnetic resonance imaging (MRI) can detect tumors consistency, but it can't predict tumor stiffness or adherence of the tumor to nearby structures. Magnetic resonance elastography (MRE) is a known non-invasive MRI based imaging technique used to assess the viscoelasticity of the tissues particularly liver fibrosis. This study discussed the importance of preoperative MRE in skull base tumors and the future implications of this new imaging modality. We did review of the English literature (by searching PubMed) regarding the use of MRE in preoperative assessment of skull base tumours stiffness and adherence to surrounding tissues. Recent research demonstrated that MRE can detect the stiffness and adherence of skull base tumors to surrounding structures by recording the spread of mechanical waves in the different tissues. In addition to non-radiation exposure, this technique is fast and can be incorporated into the conventional (MRI) study. MRE can palpate skull base tumours by imaging, allowing the stiffness of the tumour to be assessed. Preoperative assessment of brain tumours consistency, stiffness, and adherence to surrounding tissues is critical to avoid injury of important nearby structures and better preoperative patient counselling regarding surgical approach (endoscopic or open), operative time, and suspected surgical complications. However, the accuracy of MRE is less in small and highly vascular tumors. Also, MRE can't accurately detect tumour-brain adherence, but the new modality (slip-interface imaging) can. Hence, adding MRE to the conventional MRI study may help in preoperative diagnosis and treatment of skull base tumours.

Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation

BACKGROUND

Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as "soft" and "hard." This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency.

METHODS

A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed.

RESULTS

The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology.

CONCLUSIONS

We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of meningioma consistency.

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.

Relationship Between Quantitative Tumor Consistency and Pathological Factors in Intracranial Meningioma

BACKGROUND

The consistency of intracranial meningiomas is an important clinical factor because it affects the success of surgical resection. This study aimed at identifying and quantitatively measuring pathological factors that contribute to the consistency of meningiomas. Furthermore, we investigated the relationship between these factors and preoperative neuroradiological imaging.

METHODS

We analyzed 42 intracranial meningioma specimens, which had been removed at our institution between October 2012 and March 2018. Consistency was measured quantitatively after resection using an industrial stiffness meter. For pathological evaluation, we quantitatively measured the collagen-fiber content through binarization of images of Azan-Mallory-stained section. We assessed calcification and necrosis semi-quantitatively using images acquired of Hematoxylin and Eosin stained samples. The relationship between collagen-fiber content rate and imaging findings was examined.

RESULTS

The content of collagen fibers significantly positively correlated with meningioma consistency (p < 0.0001). Collagen-fiber content was significantly higher in low- and iso-intensity regions compared with high-intensity regions on the magnetic resonance T2-weighted images (p = 0.0148 and p = 0.0394, respectively). Calcification and necrosis showed no correlation with tumor consistency.

CONCLUSIONS

The quantitative hardness of intracranial meningiomas positively correlated with collagen-fiber content; thus, the amount of collagen fibers may be a factor that determines the hardness of intracranial meningiomas. Our results demonstrate that T2-weighted images reflect the collagen-fiber content and are useful for estimating tumor consistency preoperatively and non-invasively.

Noncontact longitudinal shear wave imaging for the evaluation of heterogeneous porcine brain biomechanical properties using optical coherence elastography

High-resolution quantification of heterogeneous brain biomechanical properties has long been an important topic. Longitudinal shear waves (LSWs) can be used to assess the longitudinal Young's modulus, but contact excitation methods have been used in most previous studies. We propose an air-coupled ultrasound transducer-based optical coherence elastography (AcUT-OCE) technique for noncontact excitation and detection of LSWs in samples and assessment of the nonuniformity of the brain's biomechanical properties. The air-coupled ultrasonic transducer (AcUT) for noncontact excitation of LSWs in the sample has a center frequency of 250 kHz. Phase-resolved Doppler optical coherence tomography (OCT) was used to image and reconstruct the propagation behavior of LSWs and surface ultrasound waves at high resolution. An agar phantom model was used to verify the feasibility of the experimental protocol, and experiments with ex vivo porcine brain samples were used to assess the nonuniformity of the brain biomechanical properties. LSWs with velocities of 0.83 ± 0.11 m/s were successfully excited in the agar phantom model. The perivascular elastography results in the prefrontal cortex (PFC) of the ex vivo porcine brains showed that the Young's modulus was significantly higher in the longitudinal and transverse directions on the left side of the cerebral vessels than on the right side and that the Young's modulus of the PFC decreased with increasing depth. The AcUT-OCE technique, as a new scheme for LSW applications in in vivo elastography, can be used for noncontact excitation of LSWs in brain tissue and high-resolution detection of heterogeneous brain biomechanical properties.

Mechanical properties of the brain: Focus on the essential role of Piezo1-mediated mechanotransduction in the CNS

BACKGROUND

The brain is a highly mechanosensitive organ, and changes in the mechanical properties of brain tissue influence many physiological and pathological processes. Piezo type mechanosensitive ion channel component 1 (Piezo1), a protein found in metazoans, is highly expressed in the brain and involved in sensing changes of the mechanical microenvironment. Numerous studies have shown that Piezo1-mediated mechanotransduction is closely related to glial cell activation and neuronal function. However, the precise role of Piezo1 in the brain requires further elucidation.

OBJECTIVE

This review first discusses the roles of Piezo1-mediated mechanotransduction in regulating the functions of a variety of brain cells, and then briefly assesses the impact of Piezo1-mediated mechanotransduction on the progression of brain dysfunctional disorders.

CONCLUSIONS

Mechanical signaling contributes significantly to brain function. Piezo1-mediated mechanotransduction regulates processes such as neuronal differentiation, cell migration, axon guidance, neural regeneration, and oligodendrocyte axon myelination. Additionally, Piezo1-mediated mechanotransduction plays significant roles in normal aging and brain injury, as well as the development of various brain diseases, including demyelinating diseases, Alzheimer's disease, and brain tumors. Investigating the pathophysiological mechanisms through which Piezo1-mediated mechanotransduction affects brain function will give us a novel entry point for the diagnosis and treatment of numerous brain diseases.

Differential effect of dementia etiology on cortical stiffness as assessed by MR elastography

BACKGROUND

Aging and dementia involve the disruption of brain molecular pathways leading to the alterations in tissue composition and gross morphology of the brain. Phenotypic and biomarker overlap between various etiologies of dementia supports a need for new modes of information to more accurately distinguish these disorders. Brain mechanical properties, which can be measured noninvasively by MR elastography, represent one understudied feature that are sensitive to neurodegenerative processes. In this study, we used two stiffness estimation schemes to test the hypothesis that different etiologies of dementia are associated with unique patterns of mechanical alterations across the cerebral cortex.

METHODS

MR elastography data were acquired for six clinical groups including amyloid-negative cognitively unimpaired (CU), amyloid-positive cognitively unimpaired (A + CU), amyloid-positive participants with mild cognitive impairment (A + MCI), amyloid-positive participants with Alzheimer's clinical syndrome (A + ACS), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). Stiffness maps were computed using two neural network inversions with the objective to at least partially separate the parenchyma-specific and morphological effects of neurodegeneration on mechanical property estimates. A tissue-confined inversion algorithm was designed to obtain the best estimate of stiffness in the brain parenchyma itself, while a regionally-aware inversion algorithm was used to measure the tissue stiffness along with the surroundings. Mean stiffness of 15 bilateral gray matter cortical regions were considered for statistical analysis. First, we tested the hypothesis that cortical stiffness changes in the aging brain. Next, we tested the overall study hypothesis by first comparing stiffness in each clinical group to the CU group, and then comparing the clinical groups against one another. Finally, we assessed the spatial and statistical overlap between atrophy and stiffness changes for both inversions.

RESULTS

Cortical brain regions become softer with age for both inversions with larger effects observed using regionally-aware stiffness. Stiffness decreases in the range 0.010-0.027 kPa per year were observed. Pairwise comparisons of each clinical group with cognitively unimpaired participants demonstrated 5 statistically significant differences in stiffness for tissue-confined measurements and 19 statistically different stiffness changes for the regionally-aware stiffness measurements. Pairwise comparisons between clinical groups further demonstrated unique patterns of stiffness differences. Analysis of the atrophy-versus-stiffness relationship showed that regionally-aware stiffness measurements exhibit higher sensitivity to neurodegeneration with findings that are not fully explained by partial volume effects or atrophy.

CONCLUSIONS

Both tissue-confined and regionally-aware stiffness estimates exhibited unique and complementary stiffness differences in various etiologies of dementia. Our results suggest that mechanical alterations measured by MRE reflect both tissue-specific differences as well as environmental effects. Multi-inversion schemes in MRE may provide new insights into the relationships between neuropathology and brain biomechanics.

Clinical studies of magnetic resonance elastography from 1995 to 2021: Scientometric and visualization analysis based on CiteSpace

BACKGROUND

To assess the knowledge framework around magnetic resonance elastography (MRE) and to explore MRE research hotspots and emerging trends.

METHODS

The Science Citation Index Expanded of the Web of Science Core Collection was searched on 22 October 2021 for MRE-related studies published between 1995 and 2021. Excel 2016 and CiteSpace V (version 5.8.R3) were used to analyze the downloaded data.

RESULTS

In all, 1,236 articles published by 726 authors from 540 institutions in 40 countries were included in this study. The top 10 authors published 57.6% of all included articles. The 3 most productive countries were the USA (n=631), Germany (n=202), and France (n=134), and the 3 most productive institutions were the Mayo Clinic (n=240), Charité (n=131), and the University of Illinois (n=56). The USA and the Mayo Clinic had the highest betweenness centrality among countries and institutions, respectively, and played an important role in the field of MRE. In this study, the 24,347 distinct references were clustered into 48 categories via reasonable clustering using specific keywords, forming the knowledge framework. Among the 294 co-occurring keywords, "hepatic fibrosis", "stiffness", "skeletal muscle", "acoustic strain wave", "in vivo", and "non-invasive assessment" were research hotspots. "Diagnostic performance", "diagnostic accuracy", "hepatic steatosis", "chronic hepatitis B", "radiation force impulse", "children", and "echo" were frontier topics.

CONCLUSIONS

Scientometric and visualized analysis of MRE can provide information regarding the knowledge framework, research hotspots, frontier areas, and emerging trends in this field.

The complex interactions between the cellular and non-cellular components of the brain tumor microenvironmental landscape and their therapeutic implications

Glioblastoma (GBM), an aggressive high-grade glial tumor, is resistant to therapy and has a poor prognosis due to its universal recurrence rate. GBM cells interact with the non-cellular components in the tumor microenvironment (TME), facilitating their rapid growth, evolution, and invasion into the normal brain. Herein we discuss the complexity of the interactions between the cellular and non-cellular components of the TME and advances in the field as a whole. While the stroma of non-central nervous system (CNS) tissues is abundant in fibrillary collagens, laminins, and fibronectin, the normal brain extracellular matrix (ECM) predominantly includes proteoglycans, glycoproteins, and glycosaminoglycans, with fibrillary components typically found only in association with the vasculature. However, recent studies have found that in GBMs, the microenvironment evolves into a more complex array of components, with upregulated collagen gene expression and aligned fibrillary ECM networks. The interactions of glioma cells with the ECM and the degradation of matrix barriers are crucial for both single-cell and collective invasion into neighboring brain tissue. ECM-regulated mechanisms also contribute to immune exclusion, resulting in a major challenge to immunotherapy delivery and efficacy. Glioma cells chemically and physically control the function of their environment, co-opting complex signaling networks for their own benefit, resulting in radio- and chemo-resistance, tumor recurrence, and cancer progression. Targeting these interactions is an attractive strategy for overcoming therapy resistance, and we will discuss recent advances in preclinical studies, current clinical trials, and potential future clinical applications. In this review, we also provide a comprehensive discussion of the complexities of the interconnected cellular and non-cellular components of the microenvironmental landscape of brain tumors to guide the development of safe and effective therapeutic strategies against brain cancer.

Applications of elastography in operative neurosurgery: A systematic review

Elastography is an imaging technology capable of measuring tissue stiffness and consistency. The technology has achieved widespread use in the workup and management of diseases of the liver, breast, thyroid, and prostate. Although elastography is increasingly being applied in neurosurgery, it has not yet achieved widespread adoption and many clinicians remain unfamiliar with the technology. Therefore, we sought to summarize the range of applications and elastography modalities available for neurosurgery, report its effectiveness in comparison with conventional imaging methods, and offer recommendations. All full-text English-language manuscripts on the use of elastography for neurosurgical procedures were screened using the PubMed/MEDLINE, Embase, Cochrane Library, Scopus, and Web of Science databases. Thirty-two studies were included with 990 patients, including 21 studies on intracranial tumors, 5 on hydrocephalus, 4 on epilepsy, 1 on spinal cord compression, and 1 on adolescent scoliosis. Twenty studies used ultrasound elastography (USE) whereas 12 used magnetic resonance elastography (MRE). MRE studies were mostly used in the preoperative setting for assessment of lesion stiffness, tumor-brain adherence, diagnostic workup, and operative planning. USE studies were performed intraoperatively to guide resection of lesions, determine residual microscopic abnormalities, assess the tumor-brain interface, and study mechanical properties of tumors. Elastography can assist with resection of brain tissue, detection of microscopic lesions, and workup of hydrocephalus, among other applications under investigation. Its sensitivity often exceeds that of conventional MRI and ultrasound for identifying abnormal tissue and lesion margins.

Substrate viscosity impairs temozolomide-mediated inhibition of glioblastoma cells' growth

BACKGROUND

The mechanical state of the extracellular environment of the brain cells considerably affects their phenotype during the development of central nervous system (CNS) pathologies, and when the cells respond to drugs. The reports on the evaluation of the viscoelastic properties of different brain tumors have shown that both tissue stiffness and viscosity can be altered during cancer development. Although a compelling number of reports established the role of substrate stiffness on the proliferation, motility, and drug sensitivity of brain cancer cells, there is a lack of parallel data in terms of alterations in substrate viscosity.

METHODS

Based on viscoelasticity measurements of rat brain samples using strain rheometry, polyacrylamide (PAA) hydrogels mimicking elastic and viscous parameters of the tissues were prepared. Optical microscopy and flow cytometry were employed to assess the differences in glioblastoma cells morphology, proliferation, and cytotoxicity of anticancer drug temozolomide (TZM) due to increased substrate viscosity.

RESULTS

Our results indicate that changes in substrate viscosity affect the proliferation of untreated glioma cells to a lesser extent, but have a significant impact on the apoptosis-associated depolarization of mitochondria and level of DNA fragmentation. This suggests that viscosity sensing and stiffness sensing machinery can activate different signaling pathways in glioma cells.

CONCLUSION

Collected data indicate that viscosity should be considered an important parameter in in vitro polymer-based cell culture systems used for drug screening.

Mechanical Properties of the Extracellular Environment of Human Brain Cells Drive the Effectiveness of Drugs in Fighting Central Nervous System Cancers

The evaluation of nanomechanical properties of tissues in health and disease is of increasing interest to scientists. It has been confirmed that these properties, determined in part by the composition of the extracellular matrix, significantly affect tissue physiology and the biological behavior of cells, mainly in terms of their adhesion, mobility, or ability to mutate. Importantly, pathophysiological changes that determine disease development within the tissue usually result in significant changes in tissue mechanics that might potentially affect the drug efficacy, which is important from the perspective of development of new therapeutics, since most of the currently used in vitro experimental models for drug testing do not account for these properties. Here, we provide a summary of the current understanding of how the mechanical properties of brain tissue change in pathological conditions, and how the activity of the therapeutic agents is linked to this mechanical state.

Recent advances in microsystem approaches for mechanical characterization of soft biological tissues

Microsystem technologies for evaluating the mechanical properties of soft biological tissues offer various capabilities relevant to medical research and clinical diagnosis of pathophysiologic conditions. Recent progress includes (1) the development of tissue-compliant designs that provide minimally invasive interfaces to soft, dynamic biological surfaces and (2) improvements in options for assessments of elastic moduli at spatial scales from cellular resolution to macroscopic areas and across depths from superficial levels to deep geometries. This review summarizes a collection of these technologies, with an emphasis on operational principles, fabrication methods, device designs, integration schemes, and measurement features. The core content begins with a discussion of platforms ranging from penetrating filamentary probes and shape-conformal sheets to stretchable arrays of ultrasonic transducers. Subsequent sections examine different techniques based on planar microelectromechanical system (MEMS) approaches for biocompatible interfaces to targets that span scales from individual cells to organs. One highlighted example includes miniature electromechanical devices that allow depth profiling of soft tissue biomechanics across a wide range of thicknesses. The clinical utility of these technologies is in monitoring changes in tissue properties and in targeting/identifying diseased tissues with distinct variations in modulus. The results suggest future opportunities in engineered systems for biomechanical sensing, spanning a broad scope of applications with relevance to many aspects of health care and biology research.

Predicting pituitary adenoma consistency with preoperative magnetic resonance elastography

OBJECTIVE

Pituitary adenoma is one of the most common primary intracranial neoplasms. Most of these tumors are soft, but up to 17% may have a firmer consistency. Therefore, knowing the tumor consistency in the preoperative setting could be helpful. Multiple imaging methods have been proposed to predict tumor consistency, but the results are controversial. This study aimed to evaluate the efficacy of MR elastography (MRE) in predicting tumor consistency and its potential use in a series of patients with pituitary adenomas.

METHODS

Thirty-eight patients with pituitary adenomas (≥ 2.5 cm) were prospectively evaluated with MRI and MRE before surgery. Absolute MRE stiffness values and relative MRE stiffness ratios, as well as the relative ratio of T1 signal, T2 signal, and diffusion-weighted imaging apparent diffusion coefficient (ADC) values were determined prospectively by calculating the ratio of those values in the tumor to adjacent left temporal white matter. Tumors were classified into three groups according to surgical consistency (soft, intermediate, and firm). Statistical analysis was used to identify the predictive value of the different radiological parameters in determining pituitary adenoma consistency.

RESULTS

The authors included 32 (84.21%) nonfunctional and 6 (15.79%) functional adenomas. The mean maximum tumor diameter was 3.7 cm, and the mean preoperative tumor volume was 16.4 cm3. Cavernous sinus invasion was present in 20 patients (52.63%). A gross-total resection was possible in 9 (23.68%) patients. The entire cohort's mean absolute tumor stiffness value was 1.8 kPa (range 1.1-3.7 kPa), whereas the mean tumor stiffness ratio was 0.66 (range 0.37-1.6). Intraoperative tumor consistency was significantly correlated with absolute and relative tumor stiffness (p = 0.0087 and 0.007, respectively). Tumor consistency alone was not a significant factor for predicting gross-total resection. Patients with intermediate and firm tumors had more complications compared to patients with soft tumors (50.00% vs 12.50%, p = 0.02) and also had longer operative times (p = 0.0002).

CONCLUSIONS

Whereas other MRI sequences have proven to be unreliable in determining tumor consistency, MRE has been shown to be a reliable tool for predicting adenoma consistency. Preoperative knowledge of tumor consistency could be potentially useful for surgical planning, counseling about potential surgical risks, and estimating the length of operative time.

Quantifying stability of parameter estimates forin vivonearly incompressible transversely-isotropic brain MR elastography

Easily computable quality metrics for measured medical data at point-of-care are important for imaging technologies involving offline reconstruction. Accordingly, we developed a new data quality metric forin vivotransversely-isotropic (TI) magnetic resonance elastography (MRE) based on a generalization of the widely accepted octahedral shear-strain calculation. The metric uses MRE displacement data and an estimate of the TI property field to yield a 'stability map' which predicts regions of low versus high accuracy in the resulting material property reconstructions. We can also calculate an average TI parameter stability (TIPS) score over all voxels in a region of interest for a given measurement to indicate how reliable the recovered mechanical property estimate for the region is expected to be. The calculation is rapid and places little demand on computing resources compared to the computationally intensive material property reconstruction from non-linear inversion (TI-NLI) of displacement fields, making it ideal for point-of-care evaluation of data quality. We test the predictions of the stability map for both simulated phantoms andin vivohuman brain data. We used a range of different displacement datasets from vibrations applied in the anterior-posterior (AP), left-right (LR) and combined AP + LR directions. The TIPS and variability maps (noise sensitivity or variation from the mean of repeated MRE scans) were consistently anti-correlated. Notably, Spearman correlation coefficients ∣R∣>0.6 were found between variability and TIPS score for individual white matter tracts within vivodata. These observations demonstrate the reliability and promise of this data quality metric to screen data rapidly in realistic clinical MRE applications.

Multiple motion encoding in phase-contrast MRI: A general theory and application to elastography imaging

While MRI allows to encode the motion of tissue in the magnetization's phase, it remains yet a challenge to obtain high fidelity motion images due to wraps in the phase for high encoding efficiencies. Therefore, we propose an optimal multiple motion encoding method (OMME) and exemplify it in Magnetic Resonance Elastography (MRE) data. OMME is formulated as a non-convex least-squares problem for the motion using an arbitrary number of phase-contrast measurements with different motion encoding gradients (MEGs). The mathematical properties of OMME are proved in terms of standard deviation and dynamic range of the motion's estimate for arbitrary MEGs combination which are confirmed using synthetically generated data. OMME's performance is assessed on MRE data from in vivo human brain experiments and compared to dual encoding strategies. The unwrapped images are further used to reconstruct stiffness maps and compared to the ones obtained using conventional unwrapping methods. OMME allowed to successfully combine several MRE phase images with different MEGs, outperforming dual encoding strategies in either motion-to-noise ratio (MNR) or number of successfully reconstructed voxels with good noise stability. This lead to stiffness maps with greater resolution of details than obtained with conventional unwrapping methods. The proposed OMME method allows for a flexible and noise robust increase in the dynamic range and thus provides wrap-free phase images with high MNR. In MRE, the method may be especially suitable when high resolution images with high MNR are needed.

Magnetic Resonance Elastography in Intracranial Neoplasms: A Scoping Review

BACKGROUND

Magnetic resonance elastography (MRE) allows noninvasive assessment of intracranial tumor mechanics and may thus be predictive of intraoperative conditions. Variations in the use of technical terms complicate reading of current literature, and there is need of a review using consolidated nomenclature.

OBJECTIVES

We present an overview of current literature on MRE relating to human intracranial neoplasms using standardized nomenclature suggested by the MRE guidelines committee. We then discuss the implications of the findings, and suggest approaches for future research.

METHOD

We performed a systematic literature search in PubMed, Embase, and Web of Science; the articles were screened for relevance and then subjected to full text review. Technical terms were consolidated.

RESULTS

We identified 12 studies on MRE in patients with intracranial tumors, including meningiomas, glial tumors including glioblastomas, vestibular schwannomas, hemangiopericytoma, central nervous system lymphoma, pituitary macroadenomas, and brain metastases. The studies had varying objectives that included prediction of intraoperative consistency, histological separation, prediction of adhesiveness, and exploration of the mechanobiology of tumor invasiveness and malignancy. The technical terms were translated using standardized nomenclature. The literature was highly heterogeneous in terms of image acquisition techniques, post-processing, and study design and was generally limited by small and variable cohorts.

CONCLUSIONS

MRE shows potential in predicting tumor consistency, adhesion, and mechanical homogeneity. Furthermore, MRE provides insight into malignant tumor behavior and its relation to tissue mechanics. MRE is still at a preclinical stage, but technical advances, improved understanding of soft tissue rheological impact, and larger samples are likely to enable future clinical introduction.

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.

Investigating the heterogeneity of viscoelastic properties in prostate cancer using MR elastography at 9.4T in fresh prostatectomy specimens

PURPOSE

To quantify the heterogeneity of viscoelastic tissue properties in prostatectomy specimens from men with prostate cancer (PC) using MR elastography (MRE) with histopathology as reference.

METHODS

Twelve fresh prostatectomy specimens were examined in a preclinical 9.4T MRI scanner. Maps of the complex shear modulus (|G*| in kPa) with its real and imaginary part (G' and G" in kPa) were calculated at 500 Hz. Prostates were divided into 12 segments for segment-wise measurement of viscoelastic properties and histopathology. Coefficients of variation (CVs in %) were calculated for quantification of heterogeneity.

RESULTS

Group-averaged values of cancerous vs. benign segments were significantly increased: |G*| of 12.13 kPa vs. 6.14 kPa, G' of 10.84 kPa vs. 5.44 kPa and G" of 5.45 kPa vs. 2.92 kPa, all p < 0.001. In contrast, CVs were significantly increased for benign segments: 23.59% vs. 26.32% (p = 0.014) for |G*|, 27.05% vs. 37.84% (p < 0.003) for G', and 36.51% vs. 50.37% (p = 0.008) for G".

DISCUSSION

PC is characterized by a stiff yet homogeneous biomechanical signature, which may be due to the unique nondestructive growth pattern of PC with intervening stroma, providing a rigid scaffold in the affected area. In turn, increased heterogeneity in benign prostate segments may be attributable to the presence of different prostate zones with involvement by specific nonmalignant pathology.

Soft-Tissue-Mimicking Using Hydrogels for the Development of Phantoms

With the currently available materials and technologies it is difficult to mimic the mechanical properties of soft living tissues. Additionally, another significant problem is the lack of information about the mechanical properties of these tissues. Alternatively, the use of phantoms offers a promising solution to simulate biological bodies. For this reason, to advance in the state-of-the-art a wide range of organs (e.g., liver, heart, kidney as well as brain) and hydrogels (e.g., agarose, polyvinyl alcohol –PVA–, Phytagel –PHY– and methacrylate gelatine –GelMA–) were tested regarding their mechanical properties. For that, viscoelastic behavior, hardness, as well as a non-linear elastic mechanical response were measured. It was seen that there was a significant difference among the results for the different mentioned soft tissues. Some of them appear to be more elastic than viscous as well as being softer or harder. With all this information in mind, a correlation between the mechanical properties of the organs and the different materials was performed. The next conclusions were drawn: (1) to mimic the liver, the best material is 1% wt agarose; (2) to mimic the heart, the best material is 2% wt agarose; (3) to mimic the kidney, the best material is 4% wt GelMA; and (4) to mimic the brain, the best materials are 4% wt GelMA and 1% wt agarose. Neither PVA nor PHY was selected to mimic any of the studied tissues.

Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype

OBJECTIVE

To determine whether tumor shear stiffness, as measured by magnetic resonance elastography, corresponds with intratumoral consistency and histotype.

MATERIALS AND METHODS

A total of 88 patients with 89 meningiomas (grade 1, 74 typical [13 fibroblastic, 61 non-fibroblastic]; grade 2, 12 atypical; grade 3, 3 anaplastic) were prospectively studied, each undergoing preoperative MRE in conjunction with T1-, T2- and diffusion-weighted imaging. Contrast-enhanced T1-weighted sequences were also obtained. Tumor consistency was evaluated as heterogeneous or homogenous, and graded on a 5-point scale intraoperatively. MRE-determined shear stiffness was associated with tumor consistency by surgeon's evaluation and whole-slide histologic analyses.

RESULTS

Mean tumor stiffness overall was 3.81+/-1.74 kPa (range, 1.57-12.60 kPa), correlating well with intraoperative scoring (r = 0.748; p = 0.001). MRE performed well as a gauge of tumor consistency (AUC = 0.879, 95 % CI: 0.792-0.938) and heterogeneity (AUC = 0.773, 95 % CI: 0.618-0.813), significantly surpassing conventional MR techniques (DeLong test, all p < 0.001 after Bonferroni adjustment). Shear stiffness was independently correlated with both fibrous content (partial correlation coefficient = 0.752; p < 0.001) and tumor cellularity (partial correlation coefficient = 0.547; p < 0.001). MRE outperformed other imaging techniques in distinguishing fibroblastic meningiomas from other histotypes (AUC = 0.835 vs 0.513 ∼ 0.634; all p < 0.05), but showed limited ability to differentiate atypical or anaplastic meningiomas from typical meningiomas (AUC = 0.723 vs 0.616 ∼ 0.775; all p > 0.05). Small (<2.5 cm, n = 6) and intraventricular (n = 2) tumors displayed inconsistencies between MRE and surgeon's evaluation.

CONCLUSIONS

The results of this prospective study provide substantial evidence that preoperative evaluation of meningiomas with MRE can reliably characterize tumor stiffness and spatial heterogeneity to aid neurosurgical planning.

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.

Magnetic Resonance Elastography of the Brain

Magnetic resonance elastography (MRE) is an emerging noninvasive technique, an alternative to palpation for quantitative assessment of biomechanical properties of tissue. In MRE, tissue stiffness information is obtained by a 3-step process, propagating mechanical waves in the tissues, measuring the wave propagation using modified magnetic resonance (MR) pulse sequences, and generating the quantitative stiffness maps from the MR images. MRE is clinically used in patients with liver diseases, whereas its applications in other organs are still being investigated. At present, the pediatric studies are in the initial stage and preliminary results promise to provide additional information about tissue characteristics.

Usefulness of T2 Relaxation Time for Quantitative Prediction of Meningioma Consistency

BACKGROUND

Meningioma consistency is one of the most critical factors affecting the difficulty of surgery. Although many studies have attempted to predict meningioma consistency via magnetic resonance imaging findings, no definitive method has been established, because most have been based on qualitative evaluations. Therefore, the present study examined the potential of the T2 relaxation time (T2 value), a tissue-specific quantitative parameter, for assessment of meningioma consistency.

METHODS

Eighteen surgically treated meningiomas in 16 patients were included in the present study. Preoperatively, the T2 values of all meningiomas were calculated pixel by pixel, and a T2 value distribution map was generated. A total of 27 tumor specimens (multiple specimens were procured if heterogeneous) were taken from these meningiomas, with each localization identified intraoperatively using image guidance. The consistency of the specimens was measured with a durometer, originally a device for measuring the hardness of material such as elastic rubber, and their water content was subsequently measured using wet and dry measurements.

RESULTS

A significant correlation was found between the T2 values of the matched locations identified by image guidance intraoperatively and the consistency measured using the durometer (r = -0.722; P < 0.01) and the water content (r = 0.621; P = 0.01). In addition, the water content correlated significantly with the durometer consistency (r = -0.677; P < 0.01).

CONCLUSIONS

The T2 values could be a reliable quantitative predictor of meningioma consistency, and the T2 value distribution map, which elucidated the internal structure of the tumor in detail, could provide helpful information for surgical resection.

The Advance of Magnetic Resonance Elastography in Tumor Diagnosis

The change in tissue stiffness caused by pathological changes in the tissue's structure could be detected earlier, prior to the manifestation of their clinical features. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that uses low-frequency vibrations to quantitatively measure the elasticity or stiffness of tissues. In tumor tissue, stiffness is directly related to tumor development, invasion, metastasis, and chemoradiotherapy resistance. It also dictates the choice of surgical method. At present, MRE is widely used in assessing different human organs, such as the liver, brain, breast, prostate, uterus, gallbladder, and colon stiffness. In the field of oncology, MRE's value lies in tumor diagnosis (especially early diagnosis), selection of treatment method, and prognosis evaluation. This article summarizes the principle of MRE and its research and application progress in tumor diagnosis and treatment.

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.

Nonmalignant meningioma and vestibular schwannoma incidence trends in the United States, 2004-2017

BACKGROUND

Given concerns about risks associated with the growing use of mobile phones over recent decades, the authors analyzed temporal trends in incidence rates of nonmalignant meningioma and vestibular schwannoma in the United States.

METHODS

The incidence of nonmalignant meningioma and vestibular schwannoma among adults in the Surveillance, Epidemiology, and End Results 18 registries during 2004 through 2017 was evaluated according to the method of diagnosis: microscopically (MC) or radiographically confirmed (RGC). Annual percent changes (APCs) and 95% CIs were estimated using log-linear models.

RESULTS

Overall meningioma rates (n = 108,043) increased significantly from 2004 to 2009 (APC, 5.4%; 95% CI, 4.4%-6.4%) but subsequently rose at a slower pace through 2017 (APC, 1.0%; 95% CI, 0.6%-1.5%). Rates for MC meningiomas changed little from 2004 to 2017 (APC, -0.3%; 95% CI, -0.7%, 0.1%) but rose rapidly for RGC meningiomas until 2009 (APC, 9.5%; 95% CI, 7.8%-11.1%) and rose more modestly thereafter (APC, 2.3%; 95% CI, 1.5%-3.0%). Overall vestibular schwannoma rates (n = 17,475) were stable (APC, 0.4%; 95% CI, -0.2%, 1.0%), but MC vestibular schwannoma rates decreased (APC, -1.9%; 95% CI, -2.7%, -1.1%), whereas RGC vestibular schwannoma rates rose (2006-2017: APC, 1.7%; 95% CI, 0.5%-3.0%). For each tumor, the trends by diagnostic method were similar for each sex and each racial/ethnic group, but RGC diagnosis was more likely in older patients and for smaller tumors. Meningioma trends and the proportion of RGC diagnoses varied notably by registry.

CONCLUSIONS

Overall trends obscured differences by diagnostic method in this first large, detailed assessment, but the recent stable rates argue against an association with mobile phone use. Variation among registries requires evaluation to improve the registration of these nonmalignant tumors.

LAY SUMMARY

The etiology of most benign meningiomas and vestibular schwannomas is poorly understood, but concerns have been raised about whether mobile phone use contributes to risk of developing these tumors. Descriptive studies examining temporal trends could provide insight; however, globally, few registries collect these nonmalignant cases. In the United States, reporting benign meningiomas and vestibular schwannomas became required by law in 2004. This was the first large, systematic study to quantify and characterize incidence trends for meningioma and vestibular schwannoma according to whether the tumors were diagnosed microscopically or only radiographically. Differential trends across registries and by diagnostic method suggest that caution should be used when interpreting the patterns.

Measuring viscoelastic parameters in Magnetic Resonance Elastography: a comparison at high and low magnetic field intensity

Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique which involves motion-encoding MRI for the estimation of the shear viscoelastic properties of soft tissues through the study of shear wave propagation. The technique has been found informative for disease diagnosis, as well as for monitoring of the effects of therapies. The development of MRE and its validation have been supported by the use of tissue-mimicking phantoms. In this paper we present our new MRE protocol using a low magnetic field tabletop MRI device at 0.5 T and sinusoidal uniaxial excitation in a geometrical focusing condition. Results obtained for gelatin are compared to those previously obtained using high magnetic field MRE at 11.7 T. A multi-frequency investigation is also provided via a comparison of commonly used rheological models: Maxwell, Springpot, Voigt, Zener, Jeffrey, fractional Voigt and fractional Zener. Complex shear modulus values were comparable when processed from images acquired with the tabletop low field scanner and the high field scanner. This study serves as a validation of the presented tabletop MRE protocol and paves the way for MRE experiments on ex-vivo tissue samples in both normal and pathological conditions.

Relationship between Shear Stiffness Measured by MR Elastography and Perfusion Metrics Measured by Perfusion CT of Meningiomas

BACKGROUND AND PURPOSE

When managing meningiomas, intraoperative tumor consistency and histologic subtype are indispensable factors influencing operative strategy. The purposes of this study were the following: 1) to investigate the correlation between stiffness assessed with MR elastography and perfusion metrics from perfusion CT, 2) to evaluate whether MR elastography and perfusion CT could predict intraoperative tumor consistency, and 3) to explore the predictive value of stiffness and perfusion metrics in distinguishing among histologic subtypes of meningioma.

MATERIALS AND METHODS

Mean tumor stiffness and relative perfusion metrics (blood flow, blood volume, and MTT) were calculated (relative to normal brain tissue) for 14 patients with meningiomas who underwent MR elastography and perfusion CT before surgery (cohort 1). Intraoperative tumor consistency was graded by a neurosurgeon in 18 patients (cohort 2, comprising the 14 patients from cohort 1 plus 4 additional patients). The correlation between tumor stiffness and perfusion metrics was evaluated in cohort 1, as was the ability of perfusion metrics to predict intraoperative tumor consistency and discriminate histologic subtypes. Cohort 2 was analyzed for the ability of stiffness to determine intraoperative tumor consistency and histologic subtypes.

RESULTS

The relative MTT was inversely correlated with stiffness (P = .006). Tumor stiffness was positively correlated with intraoperative tumor consistency (P = .01), while perfusion metrics were not. Relative MTT significantly discriminated transitional meningioma from meningothelial meningioma (P = .04), while stiffness did not significantly differentiate any histologic subtypes.

CONCLUSIONS

In meningioma, tumor stiffness may be useful to predict intraoperative tumor consistency, while relative MTT may potentially correlate with tumor stiffness and differentiate transitional meningioma from meningothelial meningioma.

Advanced Spheroid, Tumouroid and 3D Bioprinted In-Vitro Models of Adult and Paediatric Glioblastoma

The life expectancy of patients with high-grade glioma (HGG) has not improved in decades. One of the crucial tools to enable future improvement is advanced models that faithfully recapitulate the tumour microenvironment; they can be used for high-throughput screening that in future may enable accurate personalised drug screens. Currently, advanced models are crucial for identifying and understanding potential new targets, assessing new chemotherapeutic compounds or other treatment modalities. Recently, various methodologies have come into use that have allowed the validation of complex models—namely, spheroids, tumouroids, hydrogel-embedded cultures (matrix-supported) and advanced bioengineered cultures assembled with bioprinting and microfluidics. This review is designed to present the state of advanced models of HGG, whilst focusing as much as is possible on the paediatric form of the disease. The reality remains, however, that paediatric HGG (pHGG) models are years behind those of adult HGG. Our goal is to bring this to light in the hope that pGBM models can be improved upon.

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.

Superviscous properties of the in vivo brain at large scales

There is growing awareness that brain mechanical properties are important for neural development and health. However, published values of brain stiffness differ by orders of magnitude between static measurements and in vivo magnetic resonance elastography (MRE), which covers a dynamic range over several frequency decades. We here show that there is no fundamental disparity between static mechanical tests and in vivo MRE when considering large-scale properties, which encompass the entire brain including fluid filled compartments. Using gradient echo real-time MRE, we investigated the viscoelastic dispersion of the human brain in, so far, unexplored dynamic ranges from intrinsic brain pulsations at 1 Hz to ultralow-frequency vibrations at 5, 6.25, 7.8 and 10 Hz to the normal frequency range of MRE of 40 Hz. Surprisingly, we observed variations in brain stiffness over more than two orders of magnitude, suggesting that the in vivo human brain is superviscous on large scales with very low shear modulus of 42±13 Pa and relatively high viscosity of 6.6±0.3 Pa∙s according to the two-parameter solid model. Our data shed light on the crucial role of fluid compartments including blood vessels and cerebrospinal fluid (CSF) for whole brain properties and provide, for the first time, an explanation for the variability of the mechanical brain responses to manual palpation, local indentation, and high-dynamic tissue stimulation as used in elastography.

Shear wave elastography for intracranial epidermoid tumors

BACKGROUND

Ultrasound elastography (USE) is a novel technique that assesses the mechanical properties of body tissues in real time. Based on elasticity measurements, USE enables the differentiation of tumor tissue from surrounding normal tissue.

OBJECTIVES

We aimed to evaluate an intraoperative SWE technique for differentiating tumor tissue (epidermoid cyst) from the surrounding normal brain tissue based on elastic properties.

METHODS

We prospectively report the intraoperative elasticity assessments of four patients diagnosed with epidermoid cysts. Along with standard ultrasonography, intraoperative shear wave elastography (SWE) was used to identify tumor tissue and assess the elasticity of each tumor and the surrounding normal brain.

RESULTS

USE enabled the differentiation between epidermoid cysts and the surrounding normal brain tissue in real time intraoperatively; visual data (SWE elasticity map) and quantitative data (elasticity measurements in kilopascals) were utilized to identify the epidermoid cyst based on its elastic properties. The area representing the epidermoid cyst had an increased elasticity on SWE view and high mean elasticity values (193.7 ± 70.9 kPa in case 1, 168 ± 24.5 kPa in case 2, 205.1 ± 6.7 kPa in case 3, and 101.3 ± 12.6 kPa in case 4). The area representing the adjacent normal brain tissue on SWE view had lower mean elasticity values (14.9 ± 1.9 kPa in case 1, 22.6 ± 8.3 kPa in case 2, and 23.8 ± 1.4 kPa in case 4).

CONCLUSION

This study demonstrates the feasibility and promising value of SWE as an intraoperative tool during epidermoid cyst resection. Epidermoid tissue remnants that are hidden from the microscopic view can be detected using SWE.

Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers

BACKGROUND

The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic resonance imaging and the tumor elasticity measured by intraoperative ultrasound elastography (IOUS-E) as a reference parameter.

METHODS

A retrospective analysis was performed on supratentorial meningiomas that were operated on between March 2018 and July 2020. Cases with IOUS-E studies were included. A semiquantitative analysis of elastograms was used to define the meningioma consistency. MRIs were preprocessed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression, Naive Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve and classification accuracy.

RESULTS

Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast, apparent diffusion coefficient map, and T2-weighted images. The combination of Information Gain and ReliefF filters with the Naive Bayes algorithm resulted in an area under the curve of 0.961 and classification accuracy of 94%.

CONCLUSIONS

We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative magnetic resonance imaging (T2-weighted, T1-weighted post-contrast, and apparent diffusion coefficient map).

Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging

OBJECTIVE

The consistency of meningiomas is a critical factor affecting the difficulty of resection, operative complications, and operative time. The apparent diffusion coefficient (ADC) is derived from diffusion-weighted imaging (DWI) and is calculated using two optimized b values. While the results of comparisons between the standard ADC and the consistency of meningiomas vary, the shifted ADC has been reported to be strongly correlated with liver stiffness. The purpose of the present prospective cohort study was to determine whether preoperative standard and shifted ADC maps predict the consistency of intracranial meningiomas.

METHODS

Standard (b values 0 and 1000 sec/mm2) and shifted (b values 200 and 1500 sec/mm2) ADC maps were calculated using preoperative DWI in patients undergoing resection of intracranial meningiomas. Regions of interest (ROIs) were placed within the tumor on standard and shifted ADC maps and registered on the navigation system. Tumor tissue located at the registered ROI was resected through craniotomy, and its stiffness was measured using a durometer. The cutoff point lying closest to the upper left corner of a receiver operating characteristic (ROC) curve was determined for the detection of tumor stiffness such that an ultrasonic aspirator or scissors was always required for resection. Each tumor tissue sample with stiffness greater than or equal to or less than this cutoff point was defined as hard or soft tumor, respectively.

RESULTS

For 76 ROIs obtained from 25 patients studied, significant negative correlations were observed between stiffness and the standard ADC (ρ = -0.465, p < 0.01) and the shifted ADC (ρ = -0.490, p < 0.01). The area under the ROC curve for detecting hard tumor (stiffness ≥ 20.8 kPa) did not differ between the standard ADC (0.820) and the shifted ADC (0.847) (p = 0.39). The positive predictive value (PPV) for the combination of a low standard ADC and a low shifted ADC for detecting hard tumor was 89%. The PPV for the combination of a high standard ADC and a high shifted ADC for detecting soft tumor (stiffness < 20.8 kPa) was 81%.

CONCLUSIONS

A combination of standard and shifted ADC maps derived from preoperative DWI can be used to predict the consistency of intracranial meningiomas.

Nanomechanics and Histopathology as Diagnostic Tools to Characterize Freshly Removed Human Brain Tumors

Background

The tissue-mechanics environment plays a crucial role in human brain physiological development and the pathogenesis of different diseases, especially cancer. Assessment of alterations in brain mechanical  properties during cancer progression might provide important information about possible tissue abnormalities with clinical relevance.

Methods

With atomic force microscopy (AFM), the stiffness of freshly removed human brain tumor tissue was determined on various regions of the sample and compared to the stiffness of healthy human brain tissue that was removed during neurosurgery to gain access to tumor mass. An advantage of indentation measurement using AFM is the small volume of tissue required and high resolution at the single-cell level.

Results

Our results showed great heterogeneity of stiffness within metastatic cancer or primary high-grade gliomas compared to healthy tissue. That effect was not clearly visible in lower-grade tumors like meningioma.

Conclusion

Collected data indicate that AFM might serve as a diagnostic tool in the assessment of human brain tissue stiffness in the process of recognizing tumors.

Intraoperative Strain Elastosonography in Brain Tumor Surgery

BACKGROUND

Sonoelastography is an ultrasound imaging technique able to assess mechanical properties of tissues. Strain elastography (SE) is a qualitative sonoelastographic modality with a wide range of clinical applications, but its use in brain tumor surgery has been so far very limited.

OBJECTIVE

To describe the first large-scale implementation of SE in oncological neurosurgery for lesions discrimination and characterization.

METHODS

We analyzed retrospective data from 64 patients aiming at (i) evaluating the stiffness of the lesion and of the surrounding brain, (ii) assessing the correspondence between B-mode and SE, and (iii) performing subgroup analysis for gliomas characterization.

RESULTS

(i) In all cases, we visualized the lesion and the surrounding brain with SE, permitting a qualitative stiffness assessment. (ii) In 90% of cases, lesion representations in B-mode and SE were superimposable with identical morphology and margins. In 64% of cases, lesion margins were sharper in SE than in B-mode. (iii) In 76% of cases, glioma margins were sharper in SE than in B-mode. Lesions morphology/dimensions in SE and in B-mode were superimposable in 89%. Low-grade (LGG) and high-grade (HGG) gliomas were significantly different in terms of stiffness and stiffness contrast between tumors and brain, LGG appearing stiffer while HGG softer than brain (all P < ·001). A threshold of 2.5 SE score had 85.7% sensitivity and 94.7% specificity in differentiating LGG from HGG.

CONCLUSION

SE allows to understand mechanical properties of the brain and lesions in examination and permits a better discrimination between different tissues compared to B-mode. Additionally, SE can differentiate between LGG and HGG.

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine.

Hippocampal stiffness in mesial temporal lobe epilepsy measured with MR elastography: Preliminary comparison with healthy participants

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Hippocampal stiffness in MTLE is measured with magnetic resonance elastography.

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The epileptogenic hippocampus is stiffer than non-epileptogenic hippocampus in MTLE.

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Hippocampal stiffness ratio is higher in MTLE patients than in healthy participants.

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Stiffness ratio provides additional diagnostic information to hippocampal volume.

Mesial temporal lobe epilepsy (MTLE) is the most common form of refractory epilepsy. Common imaging biomarkers are often not sensitive enough to identify MTLE sufficiently early to facilitate the greatest benefit from surgical or pharmacological intervention. The objective of this work is to establish hippocampal stiffness measured with magnetic resonance elastography (MRE) as a biomarker for MTLE; we hypothesized that the epileptogenic hippocampus in MTLE is stiffer than the non-epileptogenic hippocampus. MRE was used to measure hippocampal stiffness in a group of patients with unilateral MTLE (n = 12) and a group of healthy comparison participants (n = 13). We calculated the ratio of hippocampal stiffness ipsilateral to epileptogenesis to the contralateral side for both groups. We found a higher hippocampal stiffness ratio in patients with MTLE compared with healthy participants (1.14 v. 0.99; p = 0.004), and that stiffness ratio differentiated MTLE from control groups effectively (AUC = 0.85). Hippocampal stiffness ratio, when added to volume ratio, an established MTLE biomarker, significantly improved the ability to differentiate the two groups (p = 0.038). Stiffness measured with MRE is sensitive to hippocampal pathology in MTLE and the addition of MRE to neuroimaging assessments may improve detection and characterization of the disease.

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.

Hippocampal viscoelasticity and episodic memory performance in healthy older adults examined with magnetic resonance elastography

Episodic memory is particularly sensitive to normative aging; however, studies investigating the structure-function relationships that support episodic memory have primarily been limited to gross volumetric measures of brain tissue health. Magnetic resonance elastography (MRE) is an emerging non-invasive, high-resolution imaging technique that uniquely quantifies brain viscoelasticity, and as such, provides a more specific measure of neural microstructural integrity. Recently, a significant double dissociation between orbitofrontal cortex-fluid intelligence and hippocampal-relational memory structure-function relationships was observed in young adults, highlighting the potential of sensitive MRE measures for studying brain health and its relation to cognitive function. However, the structure-function relationship observed by MRE has not yet been explored in healthy older adults. In this study, we examined the relationship between hippocampal (HC) viscoelasticity and episodic memory in cognitively healthy adults aged 66-73 years (N = 11), as measured with the verbal-paired associates (VPA) subtest from the Wechsler Memory Scale (WMS-R). Given the particular dependence of verbal memory tasks on the left HC, unilateral HC MRE measurements were considered for the first time. A significant negative correlation was found between left HC damping ratio, ξ and VPA recall score (r_s = -0.77, p = 0.009), which is consistent with previous findings of a relationship between HC ξ and memory performance in young adults. Conversely, correlations between right HC ξ with VPA recall score were not significant. These results highlight the utility of MRE to study cognitive decline and brain aging and suggest its possible use as a sensitive imaging biomarker for memory-related impairments.

Automatic detection of the meningioma tumor firmness in MRI images

Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.

Hypercapnia increases brain viscoelasticity

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

REVIEW: MR elastography of brain tumors

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MR elastography allows non-invasive quantification of the shear modulus of tissue.

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MRE correlates with intra-operative consistency of meningiomas, pituitary adenomas.

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Reported shear modulus values are widely distributed and overlap.

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Meningiomas were the stiffest tumor-type relative to normal appearing white matter.

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Studies are needed to determine clinical applications of MRE in neuro-oncology.

MR elastography allows non-invasive quantification of the shear modulus of tissue, i.e. tissue stiffness and viscosity, information that offers the potential to guide presurgical planning for brain tumor resection. Here, we review brain tumor MRE studies with particular attention to clinical applications. Studies that investigated MRE in patients with intracranial tumors, both malignant and benign as well as primary and metastatic, were queried from the Pubmed/Medline database in August 2018. Reported tumor and normal appearing white matter stiffness values were extracted and compared as a function of tumor histopathological diagnosis and MRE vibration frequencies. Because different studies used different elastography hardware, pulse sequences, reconstruction inversion algorithms, and different symmetry assumptions about the mechanical properties of tissue, effort was directed to ensure that similar quantities were used when making inter-study comparisons. In addition, because different methodologies and processing pipelines will necessarily bias the results, when pooling data from different studies, whenever possible, tumor values were compared with the same subject's contralateral normal appearing white matter to minimize any study-dependent bias. The literature search yielded 10 studies with a total of 184 primary and metastatic brain tumor patients. The group mean tumor stiffness, as measured with MRE, correlated with intra-operatively assessed stiffness of meningiomas and pituitary adenomas. Pooled data analysis showed significant overlap between shear modulus values across brain tumor types. When adjusting for the same patient normal appearing white matter shear modulus values, meningiomas were the stiffest tumor-type. MRE is increasingly being examined for potential in brain tumor imaging and might have value for surgical planning. However, significant overlap of shear modulus values between a number of different tumor types limits applicability of MRE for diagnostic purposes. Thus, further rigorous studies are needed to determine specific clinical applications of MRE for surgical planning, disease monitoring and molecular stratification of brain tumors.