Elastography: general principles and clincial applications

Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. STATEMENT OF SIGNIFICANCE: Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity-the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

Mouse brain elastography changes with sleep/wake cycles, aging, and Alzheimer's disease

Understanding the physiological processes in aging and how neurodegenerative disorders affect cognitive function is a high priority for advancing human health. One specific area of recently enabled research is the in vivo biomechanical state of the brain. This study utilized reverberant optical coherence elastography, a high-resolution elasticity imaging method, to investigate stiffness changes during the sleep/wake cycle, aging, and Alzheimer's disease in murine models. Four-dimensional scans of 44 wildtype mice, 13 mice with deletion of aquaporin-4 water channel, and 12 mice with Alzheimer-related pathology (APP/PS1) demonstrated that (1) cortical tissue became softer (on the order of a 10% decrease in shear wave speed) when young wildtype mice transitioned from wake to anesthetized, yet this effect was lost in aging and with mice overexpressing amyloid-β or lacking the water channel AQP4. (2) Cortical stiffness increased with age in all mice lines, but wildtype mice exhibited the most prominent changes as a function of aging. The study provides novel insight into the brain's biomechanics, the constraints of fluid flow, and how the state of brain activity affects basic properties of cortical tissues.

Inspiring a convergent engineering approach to measure and model the tissue microenvironment

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.

Physics-informed UNets for discovering hidden elasticity in heterogeneous materials

Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of mechanical parameters from strain maps as input images, normal stress boundary conditions, and domain physics information. We show superior performance - both in terms of accuracy and computational cost - by El-UNet compared to fully-connected physics-informed neural networks in estimating unknown parameters and stress distributions for isotropic linear elasticity. We characterize different variations of El-UNet and propose a self-adaptive spatial loss weighting approach. To validate our inversion models, we performed various finite-element simulations of isotropic domains with heterogenous distributions of material parameters to generate synthetic data. El-UNet is faster and more accurate than the fully-connected physics-informed implementation in resolving the distribution of unknown fields. Among the tested models, the self-adaptive spatially weighted models had the most accurate reconstructions in equal computation times. The learned spatial weighting distribution visibly corresponded to regions that the unweighted models were resolving inaccurately. Our work demonstrates a computationally efficient inversion algorithm for elasticity imaging using convolutional neural networks and presents a potential fast framework for three-dimensional inverse elasticity problems that have proven unachievable through previously proposed methods.

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.

Myofascial and discogenic origins of lumbar pain: A critical review

The purpose of this three-part narrative review is to examine the anatomy of, and the research which supports, either the lumbar myofascia or intervertebral disc (IVD) as principal sources of our patient's low back pain. A comprehensive understanding of anatomical lumbar pain generators in combination with the current treatment-based classification system will further improve and enhance clinical decision-making skills. Section I reviews the anatomy of the spinal myofascia, myofascial sources of lumbar pain, and imaging of myofascial tissues. Part II reviews the anatomy of the IVD, examines the IVD as a potential lumbar pain generator, and includes detailed discussion on Nerve Growth Factor, Inflammatory Cytokines, Vertebral End Plates and Modic change, Annular tears, and Discogenic instability. Part III looks at the history of myofascial pain, lab-based research and myofascial pain, and various levels of discogenic pain provocation research including animal, laboratory and human subjects. Our review concludes with author recommendations on developing a comprehensive understanding of altered stress concentrations affecting the posterior annulus fibrosis, neo-innervation of the IVD, inflammatory cytokines, discogenic instability, and how this knowledge can complement use of the Treatment-Based Classification System.

Personalized biomechanical insights in atrial fibrillation: opportunities & challenges

INTRODUCTION

Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke.

AREAS COVERED

In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care.

EXPERT OPINION

Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.

Measuring Shear Wave Velocity in Adult Skeletal Muscle with Ultrasound 2-D Shear Wave Elastography: A Scoping Review

Ultrasound 2-D shear wave elastography (US 2D-SWE) is a non-invasive, cost-effective tool for quantifying tissue stiffness. Amidst growing interest in US 2D-SWE for musculoskeletal research, it has been recommended that shear wave velocity (SWV) should be reported instead of elastic moduli to avoid introducing unwanted error into the data. This scoping review examined the evolving use of US 2D-SWE to measure SWV in skeletal muscle and identified strengths and weaknesses to guide future research. We searched electronic databases and key review reference lists to identify articles published between January 2000 and May 2021. Two reviewers assessed the eligibility of records during title/abstract and full-text screening, and one reviewer extracted and coded the data. Sixty-six studies met the eligibility criteria, of which 58 were published in 2017 or later. We found a striking lack of consensus regarding the effects of age and sex on skeletal muscle SWV, and widely variable reliability values. Substantial differences in methodology between studies suggest a pressing need for developing standardized, validated scanning protocols. This scoping review illustrates the breadth of application for US 2D-SWE in musculoskeletal research, and the data synthesis exposed several notable inconsistencies and gaps in current literature that warrant consideration in future studies.

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio

Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximations against ground truth determined through tensile tests. Next, using data from finite element simulation of a rectangular domain containing a stiff circular inclusion, the PINN model accurately localized the inclusion and estimated both E and ν. We observed that in a heterogeneous domain, assuming a homogeneous ν distribution increases estimation error for stiffness as well as the area of the stiff inclusion, which could have clinical importance when determining size and stiffness of tumorous tissue. Finally, our model accurately captured spatial distribution of mechanical properties and the tissue interfaces on data from another computational model, simulating uniaxial loading of a rectangular hydrogel sample containing a human brain slice with distinct gray matter and white matter regions and complex geometrical features. This elasticity imaging implementation has the potential to be used in clinical imaging scenarios to reliably discover the spatial distribution of mechanical parameters and identify material interfaces such as tumors. STATEMENT OF SIGNIFICANCE: Our work is the first implementation of physics-informed neural networks to reconstruct both material parameters - Young's modulus and Poisson's ratio - and stress distributions for isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using experimental measurements and synthetic data generated using finite element modeling. Our method can be implemented in clinical elasticity imaging scenarios to improve diagnosis of tumors and for mechanical characterization of biomaterials and biological tissues in a minimally invasive manner.

Feasibility of a Deep Learning approach to estimate Shear Wave Speed using the framework of Reverberant Shear Wave Elastography: A numerical simulation study

Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate underestimations and time-consuming issues. As well, the involvement of Deep Learning into the medical imaging field with new tools to assess complex problems, makes it a great candidate to serve as a new approach for a RSWE estimator. This work addresses the application of a Deep Neural Network (DNN) for the estimation of Shear Wave Speed (SWS) maps from particle velocity using numerically simulated data. The architecture of the proposed network is based on a U-Net, which works with a custom loss function specifically adopted for the reconstruction task. Four DNNs were trained using four different databases: clean, noisy, acquired at variable frequency, and noisy and acquired at variable frequency data. After the training of the DNNs, the predicted SWS maps were evaluated based on different metrics related to segmentation, regression and similarity of images. The model for clean data showed better results with a Mean Absolute Error (MAE) of 0.011, Mean Square Error(MSE) of 0.001, modified Intersection over Union (mIoU) of 98.4%, Peak Signal to Noise Ratio (PSNR) of 32.925 and a Structural Similarity Index Measure (SSIM) of 0.99, for 250 (size of Testing Sets); while the other models delivered SSIM in the range of 0.87 to 0.96. It was concluded that noisy and clean data could be effectively handled by the model, while the other ones still need enhancement. Clinical Relevance- This work is focused on the application of a Deep Learning approach to accurately asses the Shear Wave Speed in numerical simulations of Reverberant Shear Wave Elastography approach. This novel estimator could be useful for future clinical experiments specially with real time applications to determine the status of living tissue such as detection of malignant or benign tumors located in breast cervix prostate or skin and in the diagnosis of other pathologies such us liver fibrosis.

The Revisited Frequency-Shift Method for Shear Wave Attenuation Computation and Imaging

Ultrasound (US) shear wave (SW) elastography has been widely studied and implemented on clinical systems to assess the elasticity of living organs. Imaging of SW attenuation reflecting viscous properties of tissues has received less attention. A revisited frequency shift (R-FS) method is proposed to improve the robustness of SW attenuation imaging. Performances are compared with the FS method that we originally proposed and with the two-point frequency shift (2P-FS) and attenuation measuring US SW elastography (AMUSE) methods. In the proposed R-FS method, the shape parameter of the gamma distribution fitting SW spectra is assumed to vary with distance, in contrast to FS. Second, an adaptive random sample consensus (A-RANSAC) line fitting method is used to prevent outlier attenuation values in the presence of noise. Validation was made on ten simulated phantoms with two viscosities (0.5 and 2 Pa [Formula: see text]) and different noise levels (15 to -5 dB), two experimental homogeneous gel phantoms, and six in vivo liver acquisitions on awake ducks (including three normal and three fatty duck livers). According to the conducted experiments, R-FS revealed mean reductions in coefficients of variation (CV) of 62.6% on simulations, 62.5% with phantoms, and 62.3% in vivo compared with FS. Corresponding reductions compared with 2P-FS were 45.4%, 77.1%, and 62.0%, respectively. Reductions in normalized root-mean-square errors for simulations were 63.9% and 48.7% with respect to FS and 2P-FS, respectively.

Compression optical coherence elastography versus strain ultrasound elastography for breast cancer detection and differentiation: pilot study

The aims of this study are (i) to compare ultrasound strain elastography (US-SE) and compression optical coherence elastography (C-OCE) in characterization of elastically linear phantoms, (ii) to evaluate factors that can cause discrepancy between the results of the two elastographic techniques in application to real tissues, and (iii) to compare the results of US-SE and C-OCE in the differentiation of benign and malignant breast lesions. On 22 patients, we first used standard US-SE for in vivo assessment of breast cancer before and then after the lesion excision C-OCE was applied for intraoperative visualization of margins of the tumors and assessment of their type/grade using fresh lumpectomy specimens. For verification, the tumor grades and subtypes were determined histologically. We show that in comparison to US-SE, quantitative C-OCE has novel capabilities due to its ability to locally control stress applied to the tissue and obtain local stress-strain curves. For US-SE, we demonstrate examples of malignant tumors that were erroneously classified as benign and vice versa. For C-OCE, all lesions are correctly classified in agreement with the histology. The revealed discrepancies between the strain ratio given by US-SE and ratio of tangent Young's moduli obtained for the same samples by C-OCE are explained. Overall, C-OCE enables significantly improved specificity in breast lesion differentiation and ability to precisely visualize margins of malignant tumors compared. Such results confirm high potential of C-OCE as a high-speed and accurate method for intraoperative assessment of breast tumors and detection of their margins.

Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter

Compressional or quasi-static elastography has demonstrated the capability to detect occult cancers in a variety of tissue types, however it has a serious limitation in that the resulting elastograms are generally qualitative whereas other forms of elastography, such as shear-wave, can produce absolute measures of elasticity for histopathological classification. We address this limitation by introducing a stochastic method using an extended Kalman filter and robot-assistance to obtain quantitative elastograms which are resilient to measurement noise and system uncertainty. In this paper, the probabilistic framework is described, which utilizes many ultrasound acquisitions obtained from multiple palpations, to fuse data and uncertainty from a robotic manipulator's joint encoders and force/torque sensor directly into the inverse reconstruction of the elastogram. Quantitative results are demonstrated over homogeneous and inclusion gelatin phantoms using a seven degree of freedom manipulator for a range of initial elasticity assumptions. Results imply resilience to poorly assumed initial conditions as all trials were within 5 kPa of the elasticity measured by a mechanical testing system. Moreover, the presence or absence of an inclusion is clear in all reconstructed elastograms even when artifacts are present in displacement fields, indicating further robustness to measurement noise. The proposed stochastic method allows fusion of data from a robot's sensors directly into compressional elastography image reconstruction which may stabilize optimization and improve accuracy. This approach provides a mathematical framework to readily incorporate measurements from additional sensors in future applications which may extend the capabilities of compressional elastography beyond that of producing quantitative elasticity measurements.

Breast Cancer Detection using Multimodal Time Series Features from Ultrasound Shear Wave Absolute Vibro-Elastography

In shear wave absolute vibro-elastography (S-WAVE), a steady-state multi-frequency external mechanical excitation is applied to tissue, while a time-series of ultrasound radio-frequency (RF) data are acquired. Our objective is to determine the potential of S-WAVE to classify breast tissue lesions as malignant or benign. We present a new processing pipeline for feature-based classification of breast cancer using S-WAVE data, and we evaluate it on a new data set collected from 40 patients. Novel bi-spectral and Wigner spectrum features are computed directly from the RF time series and are combined with textural and spectral features from B-mode and elasticity images. The Random Forest permutation importance ranking and the Quadratic Mutual Information methods are used to reduce the number of features from 377 to 20. Support Vector Machines and Random Forest classifiers are used with leave-one-patient-out and Monte Carlo cross-validations. Classification results obtained for different feature sets are presented. Our best results (95% confidence interval, Area Under Curve = 95%1.45%, sensitivity = 95%, and specificity = 93%) outperform the state-of-the-art reported S-WAVE breast cancer classification performance. The effect of feature selection and the sensitivity of the above classification results to changes in breast lesion contours is also studied. We demonstrate that time-series analysis of externally vibrated tissue as an elastography technique, even if the elasticity is not explicitly computed, has promise and should be pursued with larger patient datasets. Our study proposes novel directions in the field of elasticity imaging for tissue classification.

Time-of-flight and noise-correlation-inspired algorithms for full-field shear-wave elastography using digital holography

SIGNIFICANCE

Quantitative stiffness information can be a powerful aid for tumor or fibrosis diagnosis. Currently, very promising elastography approaches developed for non-contact biomedical imaging are based on transient shear-waves imaging. Transient elastography offers quantitative stiffness information by tracking the propagation of a wave front. The most common method used to compute stiffness from the acquired propagation movie is based on shear-wave time-of-flight calculations.

AIM

We introduce an approach to transient shear-wave elastography with spatially coherent sources, able to yield full-field quantitative stiffness maps with reduced artifacts compared to typical artifacts observed in time-of-flight.

APPROACH

A noise-correlation algorithm developed for passive elastography is adapted to spatially coherent narrow or any band sources. This noise-correlation-inspired (NCi) method is employed in parallel with a classic time-of-flight approach. Testing is done on simulation images, experimental validation is conducted with a digital holography setup on controlled homogeneous samples, and full-field quantitative stiffness maps are presented for heterogeneous samples and ex-vivo biological tissues.

RESULTS

The NCi approach is first validated on simulations images. Stiffness images processed by the NCi approach on simulated inclusions display significantly less artifacts than with a time-of-flight reconstruction. The adaptability of the NCi algorithm to narrow or any band shear-wave sources was tested successfully. Experimental testing on homogeneous samples demonstrates similar values for both the time-of-flight and the NCi approach. Soft inclusions in agarose sample could be resolved using the NCi method and feasibility on ex-vivo biological tissues is presented.

CONCLUSIONS

The presented NCi approach was successful in computing quantitative full-field stiffness maps with narrow and broadband source signals on simulation and experimental images from a digital holography setup. Results in heterogeneous media show that the NCi approach could provide stiffness maps with less artifacts than with time-of-flight, demonstrating that a NCi algorithm is a promising approach for shear-wave transient elastography with spatially coherent sources.

Imaging methods to evaluate tumor microenvironment factors affecting nanoparticle drug delivery and antitumor response

Standard small molecule and nanoparticulate chemotherapies are used for cancer treatment; however, their effectiveness remains highly variable. One reason for this variable response is hypothesized to be due to nonspecific drug distribution and heterogeneity of the tumor microenvironment, which affect tumor delivery of the agents. Nanoparticle drugs have many theoretical advantages, but due to variability in tumor microenvironment (TME) factors, the overall drug delivery to tumors and associated antitumor response are low. The nanotechnology field would greatly benefit from a thorough analysis of the TME factors that create these physiological barriers to tumor delivery and treatment in preclinical models and in patients. Thus, there is a need to develop methods that can be used to reveal the content of the TME, determine how these TME factors affect drug delivery, and modulate TME factors to increase the tumor delivery and efficacy of nanoparticles. In this review, we will discuss TME factors involved in drug delivery, and how biomedical imaging tools can be used to evaluate tumor barriers and predict drug delivery to tumors and antitumor response.

Shear wave elastography of the knee menisci

OBJECTIVE

To determine the ability of shear wave elastography to measure the stiffness of the knee menisci in healthy adults.

METHODS

This observational cross-sectional study evaluated knee joints in healthy adults. Shear wave elastography was used to evaluate the anterior horn of the medial menisci bilaterally. The correlations between the mean elasticity bilaterally and age, weight, height and body mass index (BMI) were calculated using Pearson's correlation coefficient test.

RESULTS

A total of 34 knee joints in 17 healthy subjects were evaluated. The mean ± SD shear elastic modulus of the anterior horn of the right medial meniscus was 24.86 ± 6.35 kPa and of the anterior horn of the left medial meniscus was 23.86 ± 4.49 kPa. A significant inverse correlation was observed between the right medial meniscus elasticity and height. Other demographic factors showed no significant relationship to the anterior horn of the right medial meniscus elasticity. A significant inverse correlation was observed between the anterior horn of the left medial meniscus elasticity and age, while a significant positive correlation was observed between left medial meniscus elasticity and BMI.

CONCLUSION

These preliminary results suggest that shear wave elastography could be a potential tool to aid in studying the stiffness of the knee menisci.

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

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

Viscoelasticity of children and adolescent brains through MR elastography

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

Breast Tumor Diagnosis Using Finite-Element Modeling Based on Clinical in vivo Elastographic Data

OBJECTIVES

This study exploited finite-element modeling (FEM) to simulate breast tissue multicompression during ultrasound elastography to classify breast tumors based on their nonlinear biomechanical properties.

METHODS

Numeric simulations were first calculated by using 3-dimensional (3D) virtual models with an assumed tumor's geometric dimensions but with actual material properties to test and validate the FEM. Further numeric simulations were used to construct 3D models based on in vivo experimental data to verify our models. The models were designed for each individual in vivo case, emphasizing the geometry, position, and biomechanical properties of the breast tissue. At different compression levels, tissue strains were analyzed between the tumors and the background normal tissues to explore their nonlinearity and classify the tumor type. Tumor classification parameters were deduced by using a power-law relationship between the applied compressive forces and strain differences.

RESULTS

Classification parameters were compared between benign and malignant tumors, for which they were found to be statistically significant in classifying the tumor types (P < .05) by both the validation and verification of FEM. We compared the classification parameters between the in vivo and FEM classifications, for which they were found to be strongly correlated (R = 0.875; P < .001), with no statistical differences between their outcomes (P = .909).

CONCLUSIONS

Good agreement between the model outcomes and the in vivo diagnostics was reported. The implemented models were validated and verified. The introduced 3D modeling method may augment elastographic methods to preliminary classify breast tumors at an early stage.

Reconstruction of Viscosity Maps in Ultrasound Shear Wave Elastography

Change in viscoelastic properties of biological tissues may often be symptomatic of a dysfunction that can be correlated to tissue pathology. Shear wave elastography is an imaging method mainly used to assess stiffness but with the potential to measure viscoelasticity of biological tissues. This can enable tissue characterization; and thus, can be used as a marker to improve diagnosis of pathological lesions. In this study, a frequency-shift method based framework is presented for the reconstruction of viscosity by analyzing the spectral properties of acoustic radiation force-induced shear waves. The aim of the study was to investigate the feasibility of viscosity reconstruction maps in homogeneous as well as heterogeneous samples. Experiments were performed in four in vitro phantoms, two ex vivo porcine liver samples, two ex vivo fatty duck liver samples, and one in vivo fatty goose liver. Successful viscosity maps were reconstructed in homogeneous and heterogeneous phantoms with embedded mechanical inclusions having different geometries. Quantitative values of viscosity obtained for two porcine liver tissues, two fatty duck liver samples, and one goose fatty liver were (mean ± SD) 0.61 ± 0.21, 0.52 ± 0.35; 1.28 ± 0.54, 1.36 ± 0.73, and 1.67 ± 0.70 Pa.s, respectively.

Elastography Can Map the Local Inverse Relationship between Shear Modulus and Drug Delivery within the Pancreatic Ductal Adenocarcinoma Microenvironment

PURPOSE

High tissue pressure prevents chemotherapeutics from reaching the core of pancreatic tumors. Therefore, targeted therapies have been developed to reduce this pressure. While point probes have shown the effectiveness of these pressure-reducing therapies via single-location estimates, ultrasound elastography is now widely available as an imaging technique to provide real-time spatial maps of shear modulus (tissue stiffness). However, the relationship between shear modulus and the underlying tumor microenvironmental causes of high tissue pressure has not been investigated. In this work, elastography was used to investigate how shear modulus influences drug delivery in situ, and how it correlates with collagen density, hyaluronic acid content, and patent vessel density-features of the tumor microenvironment known to influence tissue pressure.

EXPERIMENTAL DESIGN

Intravenous injection of verteporfin, an approved human fluorescent drug, was used in two pancreatic cancer xenograft models [AsPC-1 (n = 25) and BxPC-3 (n = 25)].

RESULTS

Fluorescence intensity was higher in AsPC-1 tumors than in BxPC-3 tumors (P < 0.0001). Comparing drug uptake images and shear wave elastographic images with histologic images revealed that: (i) drug delivery and shear modulus were inversely related, (ii) shear modulus increased linearly with increasing collagen density, and (iii) shear modulus was marginally correlated with the local assessment of hyaluronic acid content.

CONCLUSIONS

These results demonstrate that elastography could guide targeted therapy and/or identify patients with highly elevated tissue pressure.See related commentary by Nia et al., p. 2024.

Stiffness and Beyond: What MR Elastography Can Tell Us About Brain Structure and Function Under Physiologic and Pathologic Conditions

Brain magnetic resonance elastography (MRE) was developed on the basis of a desire to "palpate by imaging" and is becoming a powerful tool in the investigation of neurophysiological and neuropathological states. Measurements are acquired with a specialized MR phase-contrast pulse sequence that can detect tissue motion in response to an applied external or internal excitation. The tissue viscoelasticity is then reconstructed from the measured displacement. Quantitative characterization of brain viscoelastic behaviors provides us an insight into the brain structure and function by assessing the mechanical rigidity, viscosity, friction, and connectivity of brain tissues. Changes in these features are associated with inflammation, demyelination, and neurodegeneration that contribute to brain disease onset and progression. Here, we review the basic principles and limitations of brain MRE and summarize its current neuroanatomical studies and clinical applications to the most common neurosurgical and neurodegenerative disorders, including intracranial tumors, dementia, multiple sclerosis, amyotrophic lateral sclerosis, and traumatic brain injury. Going forward, further improvement in acquisition techniques, stable inverse reconstruction algorithms, and advanced numerical, physical, and preclinical validation models is needed to increase the utility of brain MRE in both research and clinical applications.

Elastographic Assessment of Xenograft Pancreatic Tumors

High tissue pressures prevent chemotherapeutics from reaching the parenchyma of pancreatic ductal adenocarcinoma, which makes it difficult to treat this aggressive disease. Researchers currently use invasive probes to monitor the effectiveness of pressure-reducing therapies, but this practice introduces additional complications. Here, we hypothesize that Young's modulus is a good surrogate for tissue pressure because collagen density and hyaluoronic acid, the key features of the tumor microenvironment responsible for high tissue pressures, also affect modulus elastograms. To corroborate this hypothesis, we used model-based quasi-static elastography to assess how the Young's modulus of naturally occurring AsPc-1 pancreatic tumors varies with collagen density and hyaluoronic acid concentration. We observed that Young's moduli of orthotopically grown xenograft tumors were 6 kPa (p < 0.05) higher than that of their subcutaneously grown counterparts. We also observed a strong correlation between Young's modulus and regions within the tumors with high collagen (R² ≈ 0.8) and hyaluoronic acid (R² ≈ 0.6) densities. These preliminary results indicate that hyaluronic acid and collagen density, features of the pancreatic ductal adenocarcinoma tumor microenvironment responsible for high tissue pressure, influence Young's modulus.

Evaluation of Antivascular Combretastatin A4 P Efficacy Using Supersonic Shear Imaging Technique of Ectopic Colon Carcinoma CT26

A recent ultrasound imaging technique-shear wave elastography-showed its ability to image and quantify the mechanical properties of biological tissues, such as prostate or liver tissues. In the present study this technique was used to evaluate the relationship among tumor growth, stiffness and reduction of treatment with combretastatin (CA4 P) in allografted colon tumor CT26 in mice. During 12 d, CT26 tumor growth (n = 52) was imaged by ultrasound, and shear modulus was quantified, showing a good correlation between tumor volume and stiffness (r = 0.59). The treatment was initiated at d 12 and monitored every d during 4 d. Following the treatment, the tumor volume had decreased, while the elasticity of the tumor volume remained steady throughout the treatment. After segmentation using the shear modulus map, a detailed analysis showed a decrease in the stiffness after treatment. This reduction in the mechanical properties was shown to correlate with tissue reorganization, particularly, fibrosis and necrosis, assessed by histology.

Multimodal ultrasound computer-assisted tomography: An approach to the recognition of breast lesions

The research here presented aims at developing a new, promising method for the imaging diagnostics of breast lesions - ultrasound tomography (UST). A currently implemented device enables the reconstruction three complementary images for each coronal breast section. Based on in vivo breast examination, the authors developed an approach to breast diagnostics which employs ultrasound transmission and reflection tomography imaging. The obtained transmission images of ultrasound speed distribution show good correlation of glandular tissue areas with their visualization on MRI images. The results also allow a prediction that the implemented UST device offering fusion of reconstructed images may contribute to achieving a new standard for breast cancer diagnostics through non-invasive, fast and cheap screening tests.

Simplifying the assessment of human breast cancer by mapping a micro-scale heterogeneity index in optical coherence elastography

Surgical treatment of breast cancer aims to identify and remove all malignant tissue. Intraoperative assessment of tumor margins is, however, not exact; thus, re-excision is frequently needed, or excess normal tissue is removed. Imaging methods applicable intraoperatively could help to reduce re-excision rates whilst minimizing removal of excess healthy tissue. Optical coherence elastography (OCE) has been proposed for use in breast-conserving surgery; however, intraoperative interpretation of complex OCE images may prove challenging. Observations of breast cancer on multiple length scales, by OCE, ultrasound elastography, and atomic force microscopy, have shown an increase in the mechanical heterogeneity of malignant breast tumors compared to normal breast tissue. In this study, a micro-scale mechanical heterogeneity index is introduced and used to form heterogeneity maps from OCE scans of 10 ex vivo human breast tissue samples. Through comparison of OCE, optical coherence tomography images, and corresponding histology, malignant tissue is shown to possess a higher heterogeneity index than benign tissue. The heterogeneity map simplifies the contrast between tumor and normal stroma in breast tissue, facilitating the rapid identification of possible areas of malignancy, which is an important step towards intraoperative margin assessment using OCE.

Multi-functional Ultrasonic Micro-elastography Imaging System

In clinical decision making, in addition to anatomical information, biomechanical properties of soft tissues may provide additional clues for disease diagnosis. Given the fact that most of diseases are originated from micron sized structures, an elastography imaging system of fine resolution (~100 µm) and deep penetration depth capable of providing both qualitative and quantitative measurements of biomechanical properties is desired. Here, we report a newly developed multi-functional ultrasonic micro-elastography imaging system in which acoustic radiation force impulse imaging (ARFI) and shear wave elasticity imaging (SWEI) are implemented. To accomplish this, the 4.5 MHz/40 MHz transducer were used as the excitation/detection source, respectively. The imaging system was tested with tissue-mimicking phantoms and an ex vivo chicken liver through 2D/3D imaging. The measured lateral/axial elastography resolution and field of view are 223.7 ± 20.1/109.8 ± 6.9 µm and 1.5 mm for ARFI, 543.6 ± 39.3/117.6 ± 8.7 µm and 2 mm for SWEI, respectively. These results demonstrate that the promising capability of this high resolution elastography imaging system for characterizing tissue biomechanical properties at microscale level and its translational potential into clinical practice.

Importance of Sonoelastography in Assessing Non-Thyroid Neck Masses

Objective

To evaluate the feasibility and value of sonoelastography in assessing non-thyroid neck masses.

Methods

Non-thyroid neck masses requiring surgical interventions were evaluated using conventional B-mode ultrasonography (US) (size, short/long axis rate, shape, hilum, echogenity, calcification, necrosis, and peripheral edema) and sonoelastograpy (SE) with strain ratio (SR) and elasticity score (ES) before surgery. These parameters were compared with the histopathological examination.

Results

In total, 116 non-thyroid neck masses (66 lymph node, 35 parotid gland, eight submandibular gland, and seven cervical mass) of 89 patients (51 men, 38 women) with a mean age of 50.3±15.1 (19-79) years were evaluated. Thirty-seven malignant lymph nodes (23 metastatic and 14 lymphoma), seven malignant parotid tumors, two malignant submandibular tumors, 29 benign lymph nodes, 28 benign parotid lesions, and six benign submandibular lesions were evaluated. Mean SR and ES values of malignant masses were 6.3/3.2 for lymph nodes, 5.5/3.3 for the parotid gland, and 4.2/3 for the submandibular gland. Mean SR and ES values of benign lesions were 2.0/2.1 for lymph nodes, 4.4/3.2 for the parotid gland, and 3.2/3 for the submandibular gland. SR and ES were significantly higher for malignant masses compared with those for benign ones. SR was more predictive than ES in evaluating malignant lymph nodes. The area under the curve was 0.917(0.827-1.00) (p<0.05) for SR in differentiating benign-malignant lymph nodes, and the upper cut-off value was two. SR and ES were higher in the malign parotid and submandibular gland lesions than the benign ones, but the difference was not statistically significant.

Conclusion

Strain ratio value could be a useful parameter in differentiating benign-malignant lymph nodes. More studies are necessary for differentiating benign-malignant parotid and submandibular lesions using SE.

Investigation of optical coherence micro-elastography as a method to visualize micro-architecture in human axillary lymph nodes

Background

Evaluation of lymph node involvement is an important factor in detecting metastasis and deciding whether to perform axillary lymph node dissection (ALND) in breast cancer surgery. As ALND is associated with potentially severe long term morbidity, the accuracy of lymph node assessment is imperative in avoiding unnecessary ALND. The mechanical properties of malignant lymph nodes are often distinct from those of normal nodes. A method to image the micro-scale mechanical properties of lymph nodes could, thus, provide diagnostic information to aid in the assessment of lymph node involvement in metastatic cancer. In this study, we scan axillary lymph nodes, freshly excised from breast cancer patients, with optical coherence micro-elastography (OCME), a method of imaging micro-scale mechanical strain, to assess its potential for the intraoperative assessment of lymph node involvement.

Methods

Twenty-six fresh, unstained lymph nodes were imaged from 15 patients undergoing mastectomy or breast-conserving surgery with axillary clearance. Lymph node specimens were bisected to allow imaging of the internal face of each node. Co-located OCME and optical coherence tomography (OCT) scans were taken of each sample, and the results compared to standard post-operative hematoxylin-and-eosin-stained histology.

Results

The optical backscattering signal provided by OCT alone may not provide reliable differentiation by inspection between benign and malignant lymphoid tissue. Alternatively, OCME highlights local changes in tissue strain that correspond to malignancy and are distinct from strain patterns in benign lymphoid tissue. The mechanical contrast provided by OCME complements the optical contrast provided by OCT and aids in the differentiation of malignant tumor from uninvolved lymphoid tissue.

Conclusion

The combination of OCME and OCT images represents a promising method for the identification of malignant lymphoid tissue. This method shows potential to provide intraoperative assessment of lymph node involvement, thus, preventing unnecessary removal of uninvolved tissues and improving patient outcomes.

Ultrasound Shear Wave Viscoelastography: Model-Independent Quantification of the Complex Shear Modulus

Ultrasound shear wave elastography methods are commonly used for estimation of mechanical properties of soft biological tissues in diagnostic medicine. A limitation of most currently used elastography methods is that they yield only the shear storage modulus ( G^' ) but not the loss modulus ( G^'' ). Therefore, no information on viscosity or loss tangent (tan δ) is provided. In this paper, an ultrasound shear wave viscoelastography method is developed for model-independent quantification of frequency-dependent viscoelastic complex shear modulus of macroscopically homogeneous tissues. Three in vitro tissue-mimicking phantoms and two ex vivo porcine liver samples were evaluated. Shear waves were remotely induced within the samples using several acoustic radiation force pushes to generate a semicylindrical wave field similar to those generated by most clinically used elastography systems. The complex shear modulus was estimated over a broad frequency range (up to 1000 Hz) through the analytical solution of the developed inverse wave propagation problem using the measured shear wave speed and amplitude decay versus propagation distance. The shear storage and loss moduli obtained for the in vitro phantoms were compared with those from a planar shear wave method and the average differences over the whole frequency range studied were smaller than 7% and 15%, respectively. The reliability of the proposed method highlights its potential for viscoelastic tissue characterization, which may improve noninvasive diagnosis.

Review of ultrasound image guidance in external beam radiotherapy part II: intra-fraction motion management and novel applications

Imaging has become an essential tool in modern radiotherapy (RT), being used to plan dose delivery prior to treatment and verify target position before and during treatment. Ultrasound (US) imaging is cost-effective in providing excellent contrast at high resolution for depicting soft tissue targets apart from those shielded by the lungs or cranium. As a result, it is increasingly used in RT setup verification for the measurement of inter-fraction motion, the subject of Part I of this review (Fontanarosa et al 2015 Phys. Med. Biol. 60 R77-114). The combination of rapid imaging and zero ionising radiation dose makes US highly suitable for estimating intra-fraction motion. The current paper (Part II of the review) covers this topic. The basic technology for US motion estimation, and its current clinical application to the prostate, is described here, along with recent developments in robust motion-estimation algorithms, and three dimensional (3D) imaging. Together, these are likely to drive an increase in the number of future clinical studies and the range of cancer sites in which US motion management is applied. Also reviewed are selections of existing and proposed novel applications of US imaging to RT. These are driven by exciting developments in structural, functional and molecular US imaging and analytical techniques such as backscatter tissue analysis, elastography, photoacoustography, contrast-specific imaging, dynamic contrast analysis, microvascular and super-resolution imaging, and targeted microbubbles. Such techniques show promise for predicting and measuring the outcome of RT, quantifying normal tissue toxicity, improving tumour definition and defining a biological target volume that describes radiation sensitive regions of the tumour. US offers easy, low cost and efficient integration of these techniques into the RT workflow. US contrast technology also has potential to be used actively to assist RT by manipulating the tumour cell environment and by improving the delivery of radiosensitising agents. Finally, US imaging offers various ways to measure dose in 3D. If technical problems can be overcome, these hold potential for wide-dissemination of cost-effective pre-treatment dose verification and in vivo dose monitoring methods. It is concluded that US imaging could eventually contribute to all aspects of the RT workflow.

A survey of ultrasound elastography approaches to percutaneous ablation monitoring

Percutaneous thermal ablation has been widely used as a minimally invasive treatment for tumors. Treatment monitoring is essential for preventing complications while ensuring treatment efficacy. Mechanical testing measurements on tissue reveal that tissue stiffness increases with temperature and ablation duration. Different types of imaging methods can be used to monitor ablation procedures, including temperature or thermal strain imaging, strain imaging, modulus imaging, and shear modulus imaging. Ultrasound elastography demonstrates the potential to become the primary imaging modality for monitoring percutaneous ablation. This review briefly presented the state-of-the-art ultrasound elastography approaches for monitoring radiofrequency ablation and microwave ablation. These techniques were divided into four groups: quasi-static elastography, acoustic radiation force elastography, sonoelastography, and applicator motion elastography. Their advantages and limitations were compared and discussed. Future developments were proposed with respect to heat-induced bubbles, tissue inhomogeneities, respiratory motion, three-dimensional monitoring, multi-parametric monitoring, real-time monitoring, experimental data center for percutaneous ablation, and microwave ablation monitoring.

A regularization-free elasticity reconstruction method for ultrasound elastography with freehand scan

BACKGROUND

In ultrasound elastography, reconstruction of tissue elasticity (e.g., Young's modulus) requires regularization and known information of forces and/or displacements on tissue boundaries. In practice, it is challenging to choose an appropriate regularization parameter; and the boundary conditions are difficult to obtain in vivo. The purpose of this study is to develop a more applicable algorithm that does not need any regularization or boundary force/displacement information.

METHODS

The proposed method adopts the bicubic B-spline as the tissue motion model to estimate the displacement fields. Then the estimated displacements are input to the finite element inversion scheme to reconstruct the Young's modulus of each element. In the inversion, a modulus boundary condition is used instead of force/displacement boundary conditions. Simulation and experiments on tissue-mimicking phantoms are carried out to test the proposed method.

RESULTS

The simulation results demonstrate that Young's modulus reconstruction of the proposed method has a relative error of -3.43 ± 0.43% and root-squared-mean error of 16.94 ± 0.25%. The phantom experimental results show that the target hardening artifacts in the strain images are significantly reduced in the Young's modulus images. In both simulation and phantom studies, the size and position of inclusions can be accurately depicted in the modulus images.

CONCLUSIONS

The proposed method can reconstruct tissue Young's modulus distribution with a high accuracy. It can reduce the artifacts shown in the strain image and correctly delineate the locations and sizes of inclusions. Unlike most modulus reconstruction methods, it does not need any regularization during the inversion procedure. Furthermore, it does not need to measure the boundary conditions of displacement or force. Thus this method can be used with a freehand scan, which facilitates its usage in the clinic.

Rheology over five orders of magnitude in model hydrogels: agreement between strain-controlled rheometry, transient elastography, and supersonic shear wave imaging

Shear wave elastography helps physicians to characterize pathologies by assessing biomechanical properties of soft tissues. Compared with classical rheology, these techniques allow the quantification of the mechanical properties of tissues in the frequency range of hundreds of hertz. In this paper, ultrasound elastographic measurements and classical rheology are compared over a frequency range spanning five orders of magnitude [0.01 to 1200 Hz] to characterize model gels at multiple scales. Hybrid hydrogels were specially synthesized to get a fine tuning of the material dissipative response. Strain-controlled rheology (SCR) experiments were performed to get the elastic moduli G" and loss moduli G" from 0.01 Hz to 10 Hz and were confirmed by tensile tests. Transient elastography (TE from 50 to 400 Hz) and supersonic shear imaging (SSI from 200 to 1200 Hz) were used to characterize polymers at high frequency. Two different hydrogels were tested in the ultrasound setup with different concentration of scatterers. From low-frequency measurements, elastic moduli were extrapolated at high frequency and a very good correlation was obtained between SCR and TE and between SCR and SSI (r = 0.92 and r = 0.95, respectively). This paper demonstrates the capability of shear wave elastography to accurately image rheological properties of soft tissues, to differentiate soft elastic domains from viscous ones. It also gives new insights into soft material science because it provides a rheological tool in a high-frequency domain complementary to conventional rheometry.

Three-dimensional optical coherence elastography by phase-sensitive comparison of C-scans

We present an acquisition method for optical coherence elastography (OCE) that enables acquisition of three-dimensional elastograms in 5 s, an order of magnitude faster than previously reported. In this method, based on compression elastography, the mechanical load applied to the sample is altered between acquisitions of consecutive optical coherence tomography volume scans (C-scans). The voxel-by-voxel phase difference between the volumes is used to determine the axial displacement and determining the gradient of the axial displacement versus depth gives the local axial strain. We demonstrate sub-100-microstrain sensitivity and high contrast in elastograms, acquired in 5 s, of structured phantoms and freshly excised rat muscle tissue that are comparable in strain sensitivity and dynamic range to our previously reported B-scan-based method. The much higher acquisition speed may expedite the translation of OCE to clinical and in vivo applications.