Quantitative elasticity imaging: what can and cannot be inferred from strain images

Mapping the strain-stiffening behavior of the lung and lung cancer at microscale resolution using the crystal ribcage

Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung's material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung's microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor's stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor's mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.

On the soft tissue ultrasound elastography using FEM based inversion approach

Elastography is a medical imaging modality that enables visualization of tissue stiffness. It involves quasi-static or harmonic mechanical stimulation of the tissue to generate a displacement field which is used as input in an inversion algorithm to reconstruct tissue elastic modulus. This paper considers quasi-static stimulation and presents a novel inversion technique for elastic modulus reconstruction. The technique follows an inverse finite element framework. Reconstructed elastic modulus maps produced in this technique do not depend on the initial guess, while it is computationally less involved than iterative reconstruction approaches. The method was first evaluated using simulated data (in-silico) where modulus reconstruction's sensitivity to displacement noise and elastic modulus was assessed. To demonstrate the method's performance, displacement fields of two tissue mimicking phantoms determined using three different motion tracking techniques were used as input to the developed elastography method to reconstruct the distribution of relative elastic modulus of the inclusion to background tissue. In the next stage, the relative elastic modulus of three clinical cases pertaining to liver cancer patient were determined. The obtained results demonstrate reasonably high elastic modulus reconstruction accuracy in comparison with similar direct methods. Also it is associated with reduced computational cost in comparison with iterative techniques, which suffer from convergence and uniqueness issues, following the same formulation concept. Moreover, in comparison with other methods which need initial guess, the presented method does not require initial guess while it is easy to understand and implement.

Segmenting mechanically heterogeneous domains via unsupervised learning

From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work toward developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clustering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code are published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data.

Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems

Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics-informed deep-learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the "eggshell" effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond.

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.

Learning hidden elasticity with deep neural networks

Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity-the conditions for equilibrium-can be learned by ElastNet.

Consequences of Extracellular Matrix Remodeling in Headway and Metastasis of Cancer along with Novel Immunotherapies: A Great Promise for Future Endeavor

Tissues are progressively molded by bidirectional correspondence between denizen cells and extracellular matrix (ECM) via cell-matrix connections along with ECM remodeling. The composition and association of ECM are spatiotemporally directed to control cell conduct and differentiation; however, dysregulation of ECM dynamics prompts the development of diseases, for example, cancer. Emerging information demonstrates that hypoxia may have decisive roles in metastasis. In addition, the sprawling nature of neoplastic cells and chaotic angiogenesis are increasingly influencing microcirculation as well as altering the concentration of oxygen. In various regions of the tumor microenvironment, hypoxia, an essential player in the multistep phase of cancer metastasis, is necessary. Hypoxia can be turned into an advantage for selective cancer therapy because it is much more severe in tumors than in normal tissues. Cellular matrix gives signaling cues that control cell behavior and organize cells' elements in tissue development and homeostasis. The interplay between intrinsic factors of cancer cells themselves, including their genotype and signaling networks, and extrinsic factors of tumor stroma, for example, ECM and ECM remodeling, together decide the destiny and behavior of tumor cells. Tumor matrix encourages the development, endurance, and invasion of neoplastic and immune cell activities to drive metastasis and debilitate treatment. Incipient evidence recommends essential parts of tumor ECM segments and their remodeling in controlling each progression of the cancer-immunity cycle. Scientists have discovered that tumor matrix dynamics as well as matrix remodeling in perspective to anti-tumor immune reactions are especially important for matrix-based biomarkers recognition and followed by immunotherapy and targeting specific drugs.

Fusing electrical and elasticity imaging

Electrical and elasticity imaging are promising modalities for a suite of different applications, including medical tomography, non-destructive testing and structural health monitoring. These emerging modalities are capable of providing remote, non-invasive and low-cost opportunities. Unfortunately, both modalities are severely ill-posed nonlinear inverse problems, susceptive to noise and modelling errors. Nevertheless, the ability to incorporate complimentary datasets obtained simultaneously offers mutually beneficial information. By fusing electrical and elastic modalities as a joint problem, we are afforded the possibility to stabilize the inversion process via the utilization of auxiliary information from both modalities as well as joint structural operators. In this study, we will discuss a possible approach to combine electrical and elasticity imaging in a joint reconstruction problem giving rise to novel multi-modality applications for use in both medical and structural engineering. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

Neural-network-based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility

Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter referred to as CNN-based tracking) by the computer vision community for breast ultrasound strain elastography. Elastographic datasets produced by finite element and ultrasound simulations were used to retrain three published CNN models: FlowNet-CSS, PWC-Net, and LiteFlowNet. After retraining, the three improved CNN models were evaluated using computer-simulated and tissue-mimicking phantoms, and in vivo breast ultrasound data. CNN-based tracking results were compared with two published two-dimensional (2D) speckle tracking methods: coupled tracking and GLobal Ultrasound Elastography (GLUE) methods. Our preliminary data showed that, based on the Wilcoxon rank-sum tests, the improvements due to retraining were statistically significant (p < 0.05) for all three CNN models. We also found that the PWC-Net model was the best neural network model for data investigated, and its overall performance was on par with the coupled tracking method. CNR values estimated from in vivo axial and lateral strain elastograms showed that the GLUE algorithm outperformed both the retrained PWC-Net model and the coupled tracking method, though the GLUE algorithm exhibited some biases. The PWC-Net model was also able to achieve approximately 45 frames/second for 2D speckle tracking data investigated.

Lagrangian carotid strain imaging indices normalized to blood pressure for vulnerable plaque

OBJECTIVE

Ultrasound Lagrangian carotid strain imaging (LCSI) utilizes physiological deformation caused by arterial pressure variations to generate strain tensor maps of the vessel walls and plaques. LCSI has been criticized for the lack of normalization of magnitude-based strain indices to physiological stimuli, namely blood pressure. We evaluated the impact of normalization of magnitude-based strain indices to blood pressure measured immediately after the acquisition of radiofrequency (RF) data loops for LCSI.

MATERIALS AND METHODS

A complete clinical ultrasound examination along with RF data loops for LCSI was performed on 50 patients (30 males and 20 females) who presented with >60% carotid stenosis and were scheduled for carotid endarterectomy. Cognition was assessed using the 60-minute neuropsychological test protocol.

RESULTS

For axial strains correlation of maximum accumulated strain indices (MASI), cognition scores were -0.46 for non-normalized and -0.45, -0.49, -0.37, and -0.48 for systolic, diastolic, pulse pressure, and mean arterial pressure normalized data, respectively. The corresponding area under the curve (AUC) values for classifiers designed using maximum likelihood estimation of a binormal distribution with a median-split of the executive function cognition scores were 0.73, 0.70, 0.71, 0.70, and 0.71, respectively.

CONCLUSIONS

No significant differences in the AUC estimates were obtained between normalized and non-normalized magnitude-based strain indices.

Imaging the Vocal Folds: A Feasibility Study on Strain Imaging and Elastography of Porcine Vocal Folds

Vocal folds are an essential part of human voice production. The biomechanical properties are a good indicator for pathological changes. In particular, as an oscillation system, changes in the biomechanical properties have an impact on the vibration behavior. Subsequently, those changes could lead to voice-related disturbances. However, no existing examination combines biomechanical properties and spatial imaging. Therefore, we propose an image registration-based approach, using ultrasound in order to gain this information synchronously. We used a quasi-static load to compress the tissue and measured the displacement by image registration. The strain distribution was directly calculated from the displacement field, whereas the elastic properties were estimated by a finite element model. In order to show the feasibility and reliability of the algorithm, we tested it on gelatin phantoms. Further, by examining ex vivo porcine vocal folds, we were able to show the practicability of the approach. We displayed the strain distribution in the tissue and the elastic properties of the vocal folds. The results were superimposed on the corresponding ultrasound images. The findings are promising and show the feasibility of the suggested approach. Possible applications are in improved diagnosis of voice disorders, by measuring the biomechanical properties of the vocal folds with ultrasound. The transducer will be placed on the vocal folds of the anesthetized patient, and the elastic properties will be measured. Further, the understanding of the vocal folds’ biomechanics and the voice forming process could benefit from it.

Data-Driven Elasticity Imaging Using Cartesian Neural Network Constitutive Models and the Autoprogressive Method

Quasi-static elasticity imaging techniques rely on model-based mathematical inverse methods to estimate mechanical parameters from force-displacement measurements. These techniques introduce simplifying assumptions that preclude exploration of unknown mechanical properties with potential diagnostic value. We previously reported a data-driven approach to elasticity imaging using artificial neural networks (NNs) that circumvents limitations associated with model-based inverse methods. NN constitutive models can learn stress-strain behavior from force-displacement measurements using the autoprogressive (AutoP) method without prior assumptions of the underlying constitutive model. However, information about internal structure was required. We invented Cartesian NN constitutive models (CaNNCMs) that learn the spatial variations of material properties. We are presenting the first implementation of CaNNCMs trained with AutoP to develop data-driven models of 2-D linear-elastic materials. Both simulated and experimental force-displacement data were used as input to AutoP to show that CaNNCMs are able to model both continuous and discrete material property distributions with no prior information of internal object structure. Furthermore, we demonstrate that CaNNCMs are robust to measurement noise and can reconstruct reasonably accurate Young's modulus images from a sparse sampling of measurement data. CaNNCMs are an important step toward clinical use of data-driven elasticity imaging using AutoP.

Ultrasound elastography is useful to distinguish acute and chronic deep vein thrombosis

Essentials Ultrasound elastography uses tissue deformation to assess the relative quantification of its elasticity. Compression and duplex ultrasonography may be unable to correctly determine the thrombus age. Ultrasound elastography may be useful to distinguish between acute and chronic deep vein thrombosis. The exact determination of the thrombus age could have both therapeutic and prognostic implications. BACKGROUND: Background Ultrasound elastography (UE) imaging is a novel sonographic technique that is commonly employed for relative quantification of tissue elasticity. Its applicability to venous thromboembolic events has not yet been fully established; in particular, it is unclear whether this technique may be useful in determining the age of deep vein thrombosis (DVT). Thus, the aim of this study was to assess the role of UE in distinguishing acute from chronic DVT. Methods Consecutive patients with a first unprovoked acute and chronic (3 months old) DVT of the lower limbs were analyzed. Patients with recurrent DVT or with a suspected recurrence were excluded. The mean elasticity index (EI) values of acute and chronic popliteal and femoral vein thrombosis were compared. The accuracy of the EI in distinguishing acute from chronic DVT was also assessed by measuring the sensitivity, specificity, positive and negative predictive values, and likelihood ratios. Results One-hundred and forty-nine patients (mean age 63.9 years, standard deviation 13.6; 73 males) with acute and chronic DVT were included. The mean EI of acute femoral DVT was higher than that of chronic femoral DVT (5.09 versus 2.46), and the mean EI of acute popliteal DVT was higher than that of chronic popliteal DVT (4.96 versus 2.48). An EI value of > 4 resulted in a sensitivity of 98.9% (95% confidence interval [CI] 93.3-99.9), a specificity of 99.1% (95% CI 94.8-99.9), a positive predictive value of 91.1% (95% CI 77.9-97.1), a negative predictive value of 98.6% (95% CI 91.3-99.9), a positive likelihood ratio of 13.23 (95% CI 93-653) and a negative likelihood ratio of 0.001 (95% CI 0.008-0.05) for acute DVT. Conclusions UE appears to be a promising technique for distinguishing between acute and chronic DVT. Larger prospective studies are warranted to confirm our preliminary findings.

Ultrasound elastography reliably identifies altered mechanical properties of burned soft tissues

Although burn injury to the skin and subcutaneous tissues is common in both civilian and military scenarios, a significant knowledge gap exists in quantifying changes in tissue properties as a result of burns. In this study, we present a noninvasive technique based on ultrasound elastography which can reliably assess altered nonlinear mechanical properties of a burned tissue. In particular, ex vivo porcine skin tissues have been exposed to four different burn conditions: (i) 200°F for 10s, (ii) 200°F for 30s, (iii) 450°F for 10s, and (iv) 450°F for 30s. A custom-developed instrument including a robotically controlled ultrasound probe and force sensors has been used to compress the tissue samples to compute two parameters (C₁₀ and C₂₀) of a reduced second-order polynomial hyperelastic material model. The results indicate that while the linear model parameter (C₁₀) does not show a statistically significant difference between the test conditions, the nonlinear model parameter (C₂₀) reliably identifies three (ii-iv) of the four cases (p<0.05) when comparing burned with unburned tissues with a classification accuracy of 60-87%. Additionally, softening of the tissue is observed because of the change in structure of the collagen fibers. The ultrasound elastography-based technique has potential for application under in vivo conditions, which is left for future work.

New techniques in evaluation of the cervix

The process of parturition is poorly understood, but the cervix clearly plays a key role. Because of this, recent research efforts have been directed at objective quantification of cervical remodeling. Investigation has focused on two basic areas: (1) quantification of tissue deformability and (2) presence, orientation, and/or concentration of microstructural components (e.g. collagen). Methods to quantify tissue deformability include strain elastography and shear wave elasticity imaging (SWEI). Methods to describe tissue microstructure include attenuation and backscatter. A single parameter is unlikely to describe the complexities of cervical remodeling, but combining related parameters should improve accuracy of cervical evaluation. This chapter reviews options for cervical tissue characterization.

Method for the unique identification of hyperelastic material properties using full-field measures. Application to the passive myocardium material response

Quantitative measurement of the material properties (eg, stiffness) of biological tissues is poised to become a powerful diagnostic tool. There are currently several methods in the literature to estimating material stiffness, and we extend this work by formulating a framework that leads to uniquely identified material properties. We design an approach to work with full-field displacement data-ie, we assume the displacement field due to the applied forces is known both on the boundaries and also within the interior of the body of interest-and seek stiffness parameters that lead to balanced internal and external forces in a model. For in vivo applications, the displacement data can be acquired clinically using magnetic resonance imaging while the forces may be computed from pressure measurements, eg, through catheterization. We outline a set of conditions under which the least-square force error objective function is convex, yielding uniquely identified material properties. An important component of our framework is a new numerical strategy to formulate polyconvex material energy laws that are linear in the material properties and provide one optimal description of the available experimental data. An outcome of our approach is the analysis of the reliability of the identified material properties, even for material laws that do not admit unique property identification. Lastly, we evaluate our approach using passive myocardium experimental data at the material point and show its application to identifying myocardial stiffness with an in silico experiment modeling the passive filling of the left ventricle.

Cervical Evaluation: From Ancient Medicine to Precision Medicine

Since ancient times, cervical assessment for predicting timing of delivery has relied primarily on digital (subjective) assessment of dilatation, softening, and length. To date, transvaginal ultrasound cervical length is the only one of these parameters that meets criteria for a biomarker; no objective, quantitative measure of cervical dilatation or softening has gained clinical acceptance. This review discusses how the cervix has been assessed from ancient times to the present day and how a precision medicine approach could improve understanding of not only the cervix, but also parturition in general.

Spatial distribution of airway wall displacements during breathing and bronchoconstriction measured by ultrasound elastography using finite element image registration

With every breath, the airways within the lungs are strained. This periodic stretching is thought to play an important role in determining airway caliber in health and disease. Particularly, deep breaths can mitigate excessive airway narrowing in healthy subjects, but this beneficial effect is absent in asthmatics, perhaps due to an inability to stretch the airway smooth muscle (ASM) embedded within an airway wall. The heterogeneous composition throughout an airway wall likely modulates the strain felt by the ASM but the magnitude of ASM strain is difficult to measure directly. In this study, we optimized a finite element image registration method to measure the spatial distribution of displacements and strains throughout an airway wall during pressure inflation within the physiological breathing range before and after induced narrowing with acetylcholine (ACh). The method was shown to be repeatable, and displacements estimated from different image sequences of the same deformation agreed to within 5.3μm (0.77%). We found the magnitude and spatial distribution of displacements were radially and longitudinally heterogeneous. The region in the middle layer of the airway experienced the largest radial strain due to a transmural pressure (Ptm) increase simulating tidal breathing and a deep inspiration (DI), while the region containing the ASM (i.e., closest to the lumen) strained least. During induced narrowing with ACh, we observed temporal longitudinal heterogeneity of the airway wall. After constriction, the displacements and strain are much smaller than the relaxed airway and the pattern of strains changed, suggesting the airway stiffened heterogeneously.

Contrast-Enhanced Quantitative Intravascular Elastography: The Impact of Microvasculature on Model-Based Elastography

Model-based intravascular ultrasound elastography visualizes the stress distribution within vascular tissue-information that clinicians could use to predict the propensity of atherosclerotic plaque rupture. However, there are concerns that clusters of microvessels may reduce the accuracy of the estimated stress distribution. Consequently, we have developed a contrast-enhanced intravascular ultrasound system to investigate how plaque microvasculature affects the performance of model-based elastography. In simulations, diameters of 200, 400 and 800 μm were used, where the latter diameter represented a cluster of microvessels. In phantoms, we used a microvessel with a diameter of 750 μm. Peak stress errors of 3% and 38% were incurred in the fibrous cap when stress recovery was performed with and without a priori information about microvessel geometry. The results indicate that incorporating geometric information about plaque microvasculature obtained with contrast-enhanced ultrasound imaging improves the accuracy of estimates of the stress distribution within the fibrous cap precisely.

Quantitative micro-elastography: imaging of tissue elasticity using compression optical coherence elastography

Probing the mechanical properties of tissue on the microscale could aid in the identification of diseased tissues that are inadequately detected using palpation or current clinical imaging modalities, with potential to guide medical procedures such as the excision of breast tumours. Compression optical coherence elastography (OCE) maps tissue strain with microscale spatial resolution and can delineate microstructural features within breast tissues. However, without a measure of the locally applied stress, strain provides only a qualitative indication of mechanical properties. To overcome this limitation, we present quantitative micro-elastography, which combines compression OCE with a compliant stress sensor to image tissue elasticity. The sensor consists of a layer of translucent silicone with well-characterized stress-strain behaviour. The measured strain in the sensor is used to estimate the two-dimensional stress distribution applied to the sample surface. Elasticity is determined by dividing the stress by the strain in the sample. We show that quantification of elasticity can improve the ability of compression OCE to distinguish between tissues, thereby extending the potential for inter-sample comparison and longitudinal studies of tissue elasticity. We validate the technique using tissue-mimicking phantoms and demonstrate the ability to map elasticity of freshly excised malignant and benign human breast tissues.

A PDE-Based Regularization Algorithm Toward Reducing Speckle Tracking Noise: A Feasibility Study for Ultrasound Breast Elastography

Obtaining accurate ultrasonically estimated displacements along both axial (parallel to the acoustic beam) and lateral (perpendicular to the beam) directions is an important task for various clinical elastography applications (e.g., modulus reconstruction and temperature imaging). In this study, a partial differential equation (PDE)-based regularization algorithm was proposed to enhance motion tracking accuracy. More specifically, the proposed PDE-based algorithm, utilizing two-dimensional (2D) displacement estimates from a conventional elastography system, attempted to iteratively reduce noise contained in the original displacement estimates by mathematical regularization. In this study, tissue incompressibility was the physical constraint used by the above-mentioned mathematical regularization. This proposed algorithm was tested using computer-simulated data, a tissue-mimicking phantom, and in vivo breast lesion data. Computer simulation results demonstrated that the method significantly improved the accuracy of lateral tracking (e.g., a factor of 17 at 0.5% compression). From in vivo breast lesion data investigated, we have found that, as compared with the conventional method, higher quality axial and lateral strain images (e.g., at least 78% improvements among the estimated contrast-to-noise ratios of lateral strain images) were obtained. Our initial results demonstrated that this conceptually and computationally simple method could be useful for improving the image quality of ultrasound elastography with current clinical equipment as a post-processing tool.

Carotid plaque elasticity estimation using ultrasound elastography, MRI, and inverse FEA - A numerical feasibility study

The material properties of atherosclerotic plaques govern the biomechanical environment, which is associated with rupture-risk. We investigated the feasibility of noninvasively estimating carotid plaque component material properties through simulating ultrasound (US) elastography and in vivo magnetic resonance imaging (MRI), and solving the inverse problem with finite element analysis. 2D plaque models were derived from endarterectomy specimens of nine patients. Nonlinear neo-Hookean models (tissue elasticity C1) were assigned to fibrous intima, wall (i.e., media/adventitia), and lipid-rich necrotic core. Finite element analysis was used to simulate clinical cross-sectional US strain imaging. Computer-simulated, single-slice in vivo MR images were segmented by two MR readers. We investigated multiple scenarios for plaque model elasticity, and consistently found clear separations between estimated tissue elasticity values. The intima C1 (160 kPa scenario) was estimated as 125.8 ± 19.4 kPa (reader 1) and 128.9 ± 24.8 kPa (reader 2). The lipid-rich necrotic core C1 (5 kPa) was estimated as 5.6 ± 2.0 kPa (reader 1) and 8.5 ± 4.5 kPa (reader 2). A scenario with a stiffer wall yielded similar results, while realistic US strain noise and rotating the models had little influence, thus demonstrating robustness of the procedure. The promising findings of this computer-simulation study stimulate applying the proposed methodology in a clinical setting.

The mechanical role of the cervix in pregnancy

Appropriate mechanical function of the uterine cervix is critical for maintaining a pregnancy to term so that the fetus can develop fully. At the end of pregnancy, however, the cervix must allow delivery, which requires it to markedly soften, shorten and dilate. There are multiple pathways to spontaneous preterm birth, the leading global cause of death in children less than 5 years old, but all culminate in premature cervical change, because that is the last step in the final common pathway to delivery. The mechanisms underlying premature cervical change in pregnancy are poorly understood, and therefore current clinical protocols to assess preterm birth risk are limited to surrogate markers of mechanical function, such as sonographically measured cervical length. This is what motivates us to study the cervix, for which we propose investigating clinical cervical function in parallel with a quantitative engineering evaluation of its structural function. We aspire to develop a common translational language, as well as generate a rigorous integrated clinical-engineering framework for assessing cervical mechanical function at the cellular to organ level. In this review, we embark on that challenge by describing the current landscape of clinical, biochemical, and engineering concepts associated with the mechanical function of the cervix during pregnancy. Our goal is to use this common platform to inspire novel approaches to delineate normal and abnormal cervical function in pregnancy.

Visualizing the stress distribution within vascular tissues using intravascular ultrasound elastography: a preliminary investigation

A methodology for computing the stress distribution of vascular tissue using finite element-based, intravascular ultrasound (IVUS) reconstruction elastography is described. This information could help cardiologists detect life-threatening atherosclerotic plaques and predict their propensity to rupture. The calculation of vessel stresses requires the measurement of strain from the ultrasound images, a calibrating pressure measurement and additional model assumptions. In this work, we conducted simulation studies to investigate the effect of varying the model assumptions, specifically Poisson's ratio and the outer boundary conditions, on the resulting stress fields. In both simulation and phantom studies, we created vessel geometries with two fibrous cap thicknesses to determine if we could detect a difference in peak stress (spatially) between the two. The results revealed that (i) Poisson's ratios had negligible impact on the accuracy of stress elastograms, (ii) the outer boundary condition assumption had the greatest effect on the resulting modulus and stress distributions and (iii) in simulation and in phantom experiments, our stress imaging technique was able to detect an increased peak stress for the vessel geometry with the smaller cap thickness. This work is a first step toward understanding and creating a robust stress measurement technique for evaluating atherosclerotic plaques using IVUS elastography.

Dynamic and quasi-static mechanical testing for characterization of the viscoelastic properties of human uterine tissue

Ultrasound elastography is envisioned as an optional modality to augment standard ultrasound B-mode imaging and is a promising technique to aid in detecting uterine masses which cause abnormal uterine bleeding in both pre- and post-menopausal women. In order to determine the effectiveness of strain imaging, mechanical testing to establish the elastic contrast between normal uterine tissue and stiffer masses such as leiomyomas (fibroids) and between softer pathologies such as uterine cancer and adenomyosis has to be performed. In this paper, we evaluate the stiffness of normal uterine tissue, leiomyomas, and endometrial cancers using a EnduraTEC ElectroForce (ELF) system. We quantify the viscoelastic characteristics of uterine tissue and associated pathologies globally by using two mechanical testing approaches, namely a dynamic and a quasi-static (ramp testing) approach. For dynamic testing, 21 samples obtained from 18 patients were tested. The testing frequencies were set to 1, 10, 20, and 30 Hz. We also report on stiffness variations with pre-compression from 1% to 6% for testing at 2%, 3%, and 4% strain amplitude. Our results show that human uterine tissue stiffness is both dependent on percent pre-compression and testing frequencies. For ramp testing, 20 samples obtained from 14 patients were used. A constant strain rate of 0.1% was applied and comparable results to dynamic testing were obtained. The mean modulus contrast at 2% amplitude between normal uterine tissue (the background) and leiomyomas was 2.29 and 2.17, and between the background and cancer was 0.47 and 0.39 for dynamic and ramp testing, respectively.

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.

Quantitative sparse array vascular elastography: the impact of tissue attenuation and modulus contrast on performance

Quantitative sparse array vascular elastography visualizes the shear modulus distribution within vascular tissues, information that clinicans could use to reduce the number of strokes each year. However, the low transmit power sparse array (SA) imaging could hamper the clinical usefulness of the resulting elastograms. In this study, we evaluated the performance of modulus elastograms recovered from simulated and physical vessel phantoms with varying attenuation coefficients (0.6, 1.5, and [Formula: see text]) and modulus contrasts ([Formula: see text], [Formula: see text], and [Formula: see text]) using SA imaging relative to those obtained with conventional linear array (CLA) and plane-wave (PW) imaging techniques. Plaques were visible in all modulus elastograms, but those produced using SA and PW contained less artifacts. The modulus contrast-to-noise ratio decreased rapidly with increasing modulus contrast and attenuation coefficient, but more quickly when SA imaging was performed than for CLA or PW. The errors incurred varied from 10.9% to 24% (CLA), 1.8% to 12% (SA), and [Formula: see text] (PW). Modulus elastograms produced with SA and PW imagings were not significantly different ([Formula: see text]). Despite the low transmit power, SA imaging can produce useful modulus elastograms in superficial organs, such as the carotid artery.

Boundary condition generating large strain on breast tumor for nonlinear elasticity estimation

We describe a robotic palpation system that determines whether a breast tumor is benign or malignant by measuring its nonlinear elasticity. Two indenters compress the breast from different directions to generate sufficient strain on the inner tumor, which simply represents clinical dynamic testing. The nonlinear elasticity of the inner tumor is estimated by correcting the reaction force data of the surrounding soft tissue. Here, we present the basic concept of our study and simulation results considering geometric conditions of the indenters using a finite element breast model. Indenters with variable width are applied to the breast at several contact positions in a simulation for comparison. Our results indicate that when the spring stiffness between the contact position of one indenter and the center of the tumor equals the spring stiffness between the contact position of the other indenter and the center of the tumor, a larger contact area (i.e., larger spring stiffness) provides larger strain acting on the inner tumor.

A novel fast full inversion based breast ultrasound elastography technique

Cancer detection and classification have been the focus of many imaging and therapeutic research studies. Elastography is a non-invasive technique to visualize suspicious soft tissue areas where tissue stiffness is used as image contrast mechanism. In this study, a breast ultrasound elastography system including software and hardware is proposed. Unlike current elastography systems that image the tissue strain and present it as an approximation to relative tissue stiffness, this system is capable of imaging the breast absolute Young's modulus in fast fashion. To improve the quality of elastography images, a novel system consisting of two load cells has been attached to the ultrasound probe. The load cells measure the breast surface forces to be used for calculating the tissue stress distribution throughout the breast. To facilitate fast imaging, this stress calculation is conducted by an accelerated finite element method. Acquired tissue displacements and surface force data are used as input to the proposed Young's modulus reconstruction technique. Numerical and tissue mimicking phantom studies were conducted for validating the proposed system. These studies indicated that fast imaging of breast tissue absolute Young's modulus using the proposed ultrasound elastography system is feasible. The tissue mimicking phantom study indicated that the system is capable of providing reliable absolute Young's modulus values for both normal tissue and tumour as the maximum Young's modulus reconstruction error was less than 6%. This demonstrates that the proposed system has a good potential to be used for clinical breast cancer assessment.

A regularization-free Young's modulus reconstruction algorithm for ultrasound elasticity imaging

Ultrasound elasticity imaging aims to reconstruct the distribution of elastic modulus (e.g., Young's modulus) within biological tissues, since the value of elastic modulus is often related to pathological changes. Currently, most elasticity imaging algorithms face a challenge of choosing the value of the regularization constant. We propose a more applicable algorithm without the need of any regularization. This algorithm is not only simple to use, but has a relatively high accuracy. Our method comprises of a nonrigid registration technique and tissue incompressibility assumption to estimate the two-dimensional (2D) displacement field, and finite element method (FEM) to reconstruct the Young's modulus distribution. Simulation and phantom experiments are performed to evaluate the algorithm. Simulation and phantom results showed that the proposed algorithm can reconstruct the Young's modulus with an accuracy of 63∼85%.

In vivo classification of breast masses using features derived from axial-strain and axial-shear images

Breast cancer is currently the second leading cause of cancer deaths in women. Early detection and accurate classification of suspicious masses as benign or malignant is important for arriving at an appropriate treatment plan. In this article, we present classification results for features extracted from ultrasound-based, axial-strain and axial-shear images of breast masses. The breast-mass stiffness contrast, size ratio, and a normalized axial-shear strain area feature are evaluated for the classification of in vivo breast masses using a leave-one-out classifier. Radiofrequency echo data from 123 patients were acquired using Siemens Antares or Elegra clinical ultrasound systems during freehand palpation. Data from four different institutions were analyzed. Axial displacements and strains were estimated using a multilevel, pyramid-based two-dimensional cross-correlation algorithm, with final processing block dimensions of 0.385 mm × 0.507 mm (three A-lines). Since mass boundaries on B-mode images for 21 patients could not be delineated (isoechoic), the combined feature analysis was only performed for 102 patients. Results from receiver operating characteristic (ROC) demonstrate that the area under the curve was 0.90, 0.84, and 0.52 for the normalized axial-shear strain, size ratio, and stiffness contrast, respectively. When these three features were combined using a leave-one-out classifier and support vector machine approach, the overall area under the curve improved to 0.93.

Displacement and strain estimation for evaluation of arterial wall stiffness using a familial hypercholesterolemia swine model of atherosclerosis

PURPOSE

To track variations in the deformation of the arterial wall noninvasively by estimating the accumulated displacement and strain over a cardiac cycle may provide useful indicators of vascular health.

METHODS

In this paper, we propose an approach to track a region of interest (ROI) locally and estimate arterial stiffness variation in a familial hypercholesterolemic swine model of spontaneous atherosclerosis that allows for systematic and reproducible study of progression of the disease mechanism.

RESULTS

Strain and displacement indices may be derived from the variations of the accumulated displacement and accumulated strain (obtained from the gradient of the accumulated displacement) over a cardiac cycle to predict not only the likelihood of developing vascular diseases, but also the sites where they may occur. Currently, an ROI thickness value of less than one mm within the arterial wall is necessary for the axial accumulated displacement and strain to obtain reproducible estimates.

CONCLUSIONS

Accumulated axial displacement and strain estimation on the artery wall shown in this paper indicate the repeatability of these measurements over several cardiac cycles and over five familial hypercholesterolemic swine. Our results also demonstrate the need for a small region of interest within the arterial walls for accurate and robust estimates of arterial function.

Model-based elastography: a survey of approaches to the inverse elasticity problem

Elastography is emerging as an imaging modality that can distinguish normal versus diseased tissues via their biomechanical properties. This paper reviews current approaches to elastography in three areas--quasi-static, harmonic and transient--and describes inversion schemes for each elastographic imaging approach. Approaches include first-order approximation methods; direct and iterative inversion schemes for linear elastic; isotropic materials and advanced reconstruction methods for recovering parameters that characterize complex mechanical behavior. The paper's objective is to document efforts to develop elastography within the framework of solving an inverse problem, so that elastography may provide reliable estimates of shear modulus and other mechanical parameters. We discuss issues that must be addressed if model-based elastography is to become the prevailing approach to quasi-static, harmonic and transient elastography: (1) developing practical techniques to transform the ill-posed problem with a well-posed one; (2) devising better forward models to capture the complex mechanical behavior of soft tissues and (3) developing better test procedures to evaluate the performance of modulus elastograms.

Nonlinear elasto-mammography for characterization of breast tissue properties

Quantification of the mechanical behavior of normal and cancerous tissues has important implication in the diagnosis of breast tumor. The present work extends the authors' nonlinear elastography framework to incorporate the conventional X-ray mammography, where the projection of displacement information is acquired instead of full three-dimensional (3D) vector. The elastic parameters of normal and cancerous breast tissues are identified by minimizing the difference between the measurement and the corresponding computational prediction. An adjoint method is derived to calculate the gradient of the objective function. Simulations are conducted on a 3D breast phantom consisting of the fatty tissue, glandular tissue, and cancerous tumor, whose mechanical responses are hyperelastic in nature. The material parameters are identified with consideration of measurement error. The results demonstrate that the projective displacements acquired in X-ray mammography provide sufficient constitutive information of the tumor and prove the usability and robustness of the proposed method and algorithm.

Investigating the impact of spatial priors on the performance of model-based IVUS elastography

This paper describes methods that provide pre-requisite information for computing circumferential stress in modulus elastograms recovered from vascular tissue-information that could help cardiologists detect life-threatening plaques and predict their propensity to rupture. The modulus recovery process is an ill-posed problem; therefore, additional information is needed to provide useful elastograms. In this work, prior geometrical information was used to impose hard or soft constraints on the reconstruction process. We conducted simulation and phantom studies to evaluate and compare modulus elastograms computed with soft and hard constraints versus those computed without any prior information. The results revealed that (1) the contrast-to-noise ratio of modulus elastograms achieved using the soft prior and hard prior reconstruction methods exceeded those computed without any prior information; (2) the soft prior and hard prior reconstruction methods could tolerate up to 8% measurement noise, and (3) the performance of soft and hard prior modulus elastograms degraded when incomplete spatial priors were employed. This work demonstrates that including spatial priors in the reconstruction process should improve the performance of model-based elastography, and the soft prior approach should enhance the robustness of the reconstruction process to errors in the geometrical information.

Transversely isotropic elasticity imaging of cancellous bone

To measure spatial variations in mechanical properties of biological materials, prior studies have typically performed mechanical tests on excised specimens of tissue. Less invasive measurements, however, are preferable in many applications, such as patient-specific modeling, disease diagnosis, and tracking of age- or damage-related degradation of mechanical properties. Elasticity imaging (elastography) is a nondestructive imaging method in which the distribution of elastic properties throughout a specimen can be reconstructed from measured strain or displacement fields. To date, most work in elasticity imaging has concerned incompressible, isotropic materials. This study presents an extension of elasticity imaging to three-dimensional, compressible, transversely isotropic materials. The formulation and solution of an inverse problem for an anisotropic tissue subjected to a combination of quasi-static loads is described, and an optimization and regularization strategy that indirectly obtains the solution to the inverse problem is presented. Several applications of transversely isotropic elasticity imaging to cancellous bone from the human vertebra are then considered. The feasibility of using isotropic elasticity imaging to obtain meaningful reconstructions of the distribution of material properties for vertebral cancellous bone from experiment is established. However, using simulation, it is shown that an isotropic reconstruction is not appropriate for anisotropic materials. It is further shown that the transversely isotropic method identifies a solution that predicts the measured displacements, reveals regions of low stiffness, and recovers all five elastic parameters with approximately 10% error. The recovery of a given elastic parameter is found to require the presence of its corresponding strain (e.g., a deformation that generates ɛ₁₂ is necessary to reconstruct C₁₂₁₂), and the application of regularization is shown to improve accuracy. Finally, the effects of noise on reconstruction quality is demonstrated and a signal-to-noise ratio (SNR) of 40 dB is identified as a reasonable threshold for obtaining accurate reconstructions from experimental data. This study demonstrates that given an appropriate set of displacement fields, level of regularization, and signal strength, the transversely isotropic method can recover the relative magnitudes of all five elastic parameters without an independent measurement of stress. The quality of the reconstructions improves with increasing contrast, magnitude of deformation, and asymmetry in the distributions of material properties, indicating that elasticity imaging of cancellous bone could be a useful tool in laboratory studies to monitor the progression of damage and disease in this tissue.

Medical ultrasound: imaging of soft tissue strain and elasticity

After X-radiography, ultrasound is now the most common of all the medical imaging technologies. For millennia, manual palpation has been used to assist in diagnosis, but it is subjective and restricted to larger and more superficial structures. Following an introduction to the subject of elasticity, the elasticity of biological soft tissues is discussed and published data are presented. The basic physical principles of pulse-echo and Doppler ultrasonic techniques are explained. The history of ultrasonic imaging of soft tissue strain and elasticity is summarized, together with a brief critique of previously published reviews. The relevant techniques-low-frequency vibration, step, freehand and physiological displacement, and radiation force (displacement, impulse, shear wave and acoustic emission)-are described. Tissue-mimicking materials are indispensible for the assessment of these techniques and their characteristics are reported. Emerging clinical applications in breast disease, cardiology, dermatology, gastroenterology, gynaecology, minimally invasive surgery, musculoskeletal studies, radiotherapy, tissue engineering, urology and vascular disease are critically discussed. It is concluded that ultrasonic imaging of soft tissue strain and elasticity is now sufficiently well developed to have clinical utility. The potential for further research is examined and it is anticipated that the technology will become a powerful mainstream investigative tool.

Recent results in nonlinear strain and modulus imaging

We report a summary of recent developments and current status of our team's efforts to image and quantify in vivo nonlinear strain and tissue mechanical properties. Our work is guided by a focus on applications to cancer diagnosis and treatment using clinical ultrasound imaging and quasi-static tissue deformations. We review our recent developments in displacement estimation from ultrasound image sequences. We discuss cross correlation approaches, regularized optimization approaches, guided search methods, multiscale methods, and hybrid methods. Current implementations can return results of high accuracy in both axial and lateral directions at several frames per second.We compare several strain estimators. Again we see a benefit from a regularized optimization approach. We then discuss both direct and iterative methods to reconstruct tissue mechanical property distributions from measured strain and displacement fields. We review the formulation, discretization, and algorithmic considerations that come into play when attempting to infer linear and nonlinear elastic properties from strain and displacement measurements. Finally we illustrate our progress with example applications in breast disease diagnosis and tumor ablation monitoring. Our current status shows that we have demonstrated quantitative determination of nonlinear parameters in phantoms and in vivo, in the context of 2D models and data. We look forward to incorporating 3D data from 2D transducer arrays to noninvasively create calibrated 3D quantitative maps of nonlinear elastic properties of breast tissues in vivo.

AN OVERVIEW OF ELASTOGRAPHY - AN EMERGING BRANCH OF MEDICAL IMAGING

From times immemorial manual palpation served as a source of information on the state of soft tissues and allowed detection of various diseases accompanied by changes in tissue elasticity. During the last two decades, the ancient art of palpation gained new life due to numerous emerging elasticity imaging (EI) methods. Areas of applications of EI in medical diagnostics and treatment monitoring are steadily expanding. Elasticity imaging methods are emerging as commercial applications, a true testament to the progress and importance of the field.In this paper we present a brief history and theoretical basis of EI, describe various techniques of EI and, analyze their advantages and limitations, and overview main clinical applications. We present a classification of elasticity measurement and imaging techniques based on the methods used for generating a stress in the tissue (external mechanical force, internal ultrasound radiation force, or an internal endogenous force), and measurement of the tissue response. The measurement method can be performed using differing physical principles including magnetic resonance imaging (MRI), ultrasound imaging, X-ray imaging, optical and acoustic signals.Until recently, EI was largely a research method used by a few select institutions having the special equipment needed to perform the studies. Since 2005 however, increasing numbers of mainstream manufacturers have added EI to their ultrasound systems so that today the majority of manufacturers offer some sort of Elastography or tissue stiffness imaging on their clinical systems. Now it is safe to say that some sort of elasticity imaging may be performed on virtually all types of focal and diffuse disease. Most of the new applications are still in the early stages of research, but a few are becoming common applications in clinical practice.

Estimating axial and lateral strain using a synthetic aperture elastographic imaging system

Model-based elastography is an emerging technique with clinical applications in imaging vascular tissues, guiding minimally invasive therapies and diagnosing breast and prostate cancers. Its usage is limited because ultrasound can measure only the axial component of displacement with high precision. The goal of this study was to assess the effect of lateral sampling frequency, lateral beam-width and the number of active transmission elements on the quality of axial and lateral strain elastograms. Elastographic imaging was performed on gelatin-based phantoms with a modified commercial ultrasound scanner. Three groups of radio-frequency (RF) echo frames were reconstructed from fully synthetic aperture data. In the first group, all 128 transmission elements (corresponding to a lateral beamwidth of 0.22 mm at the center of the field of view) were used to reconstruct RF echo frames with A-line densities that varied from 6.4 lines/mm to 51.2 lines/mm. In the second group, the size of the aperture was varied to produce RF echo frames with lateral beamwidths ranging from 0.22 mm to 0.43 mm and a fixed A-line density of 25.6 lines/mm. In the third group, sparse arrays with varying number of active transmission elements (from 2 to 128) were used to reconstruct RF echo frames, whose A-line density and lateral beamwidth were fixed to 25.6 lines/mm and 0.22 mm, respectively. Applying a two-dimensional (2-D) displacement estimator to the pre- and post-deformed RF echo frames produced displacement elastograms. Axial and lateral strain elastograms were computed from displacement elastograms with a least squares strain estimator. The quality of axial and lateral strain elastograms improved with increasing applied strain and A-line density but decreased with increasing lateral beamwidth and deteriorated as the number of active transmission elements in the sparse arrays were reduced. This work demonstrated that the variance incurred when estimating the lateral component of displacement was reduced considerably when elastography was performed with a synthetic aperture ultrasound imaging system. Satisfactory axial and lateral strain elastograms were produced using a sparse array with as few as 16 active transmission elements.

Real-time quasi-static ultrasound elastography

Ultrasound elastography is a technique used for clinical imaging of tissue stiffness with a conventional ultrasound machine. It was first proposed two decades ago, but active research continues in this area to the present day. Numerous clinical applications have been investigated, mostly related to cancer imaging, and though these have yet to prove conclusive, the technique has seen increasing commercial and clinical interest. This paper presents a review of the most widely adopted, non-quantitative, techniques focusing on technical innovations rather than clinical applications. The review is not intended to be exhaustive, concentrating instead on placing the various techniques in context according to the authors' perspective of the field.

What challenges must be overcome before ultrasound elasticity imaging is ready for the clinic?

Ultrasound elasticity imaging has been a research interest for the past 20 years with the goal of generating novel images of soft tissues based on their material properties (i.e., stiffness and viscosity). The motivation for such an imaging modality lies in the fact that many soft tissues can share similar ultrasonic echogenicities, but may have very different mechanical properties that can be used to clearly visualize normal anatomy and delineate diseased tissues and masses. Recently, elasticity imaging techniques have moved from the laboratory to the clinical setting, where clinicians are beginning to characterize tissue stiffness as a diagnostic metric and commercial implementations of ultrasonic elasticity imaging are beginning to appear on the market. This article provides a foundation for elasticity imaging, an overview of current research and commercial realizations of elasticity imaging technology and a perspective on the current successes, limitations and potential for improvement of these imaging technologies.

Model-based reconstructive elasticity imaging using ultrasound

Elasticity imaging is a reconstructive imaging technique where tissue motion in response to mechanical excitation is measured using modern imaging systems, and the estimated displacements are then used to reconstruct the spatial distribution of Young's modulus. Here we present an ultrasound elasticity imaging method that utilizes the model-based technique for Young's modulus reconstruction. Based on the geometry of the imaged object, only one axial component of the strain tensor is used. The numerical implementation of the method is highly efficient because the reconstruction is based on an analytic solution of the forward elastic problem. The model-based approach is illustrated using two potential clinical applications: differentiation of liver hemangioma and staging of deep venous thrombosis. Overall, these studies demonstrate that model-based reconstructive elasticity imaging can be used in applications where the geometry of the object and the surrounding tissue is somewhat known and certain assumptions about the pathology can be made.

Two-dimensional shear wave speed and crawling wave speed recoveries from in vitro prostate data

The crawling wave experiment was developed to capture a shear wave induced moving interference pattern that is created by two harmonic vibration sources oscillating at different but almost the same frequencies. Using the vibration sonoelastography technique, the spectral variance image reveals a moving interference pattern. It has been shown that the speed of the moving interference pattern, i.e., the crawling wave speed, is proportional to the shear wave speed with a nonlinear factor. This factor can generate high-speed artifacts in the crawling wave speed images that do not actually correspond to increased stiffness. In this paper, an inverse algorithm is developed to reconstruct both the crawling wave speed and the shear wave speed using the phases of the crawling wave and the shear wave. The feature for the data is the application to in vitro prostate data, while the features for the algorithm include the following: (1) A directional filter is implemented to obtain a wave moving in only one direction; and (2) an L(1) minimization technique with physics inspired constraints is employed to calculate the phase of the crawling wave and to eliminate jump discontinuities from the phase of the shear wave. The algorithm is tested on in vitro prostate data measured at the Rochester Center for Biomedical Ultrasound and University of Rochester. Each aspect of the algorithm is shown to yield image improvement. The results demonstrate that the shear wave speed images can have less artifacts than the crawling wave images. Examples are presented where the shear wave speed recoveries have excellent agreement with histology results on the size, shape, and location of cancerous tissues in the glands.

Real-time regularized ultrasound elastography

This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio-frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded.

Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer

Dynamic remodeling of the extracellular matrix (ECM) is essential for development, wound healing and normal organ homeostasis. Life-threatening pathological conditions arise when ECM remodeling becomes excessive or uncontrolled. In this Perspective, we focus on how ECM remodeling contributes to fibrotic diseases and cancer, which both present challenging obstacles with respect to clinical treatment, to illustrate the importance and complexity of cell-ECM interactions in the pathogenesis of these conditions. Fibrotic diseases, which include pulmonary fibrosis, systemic sclerosis, liver cirrhosis and cardiovascular disease, account for over 45% of deaths in the developed world. ECM remodeling is also crucial for tumor malignancy and metastatic progression, which ultimately cause over 90% of deaths from cancer. Here, we discuss current methodologies and models for understanding and quantifying the impact of environmental cues provided by the ECM on disease progression, and how improving our understanding of ECM remodeling in these pathological conditions is crucial for uncovering novel therapeutic targets and treatment strategies. This can only be achieved through the use of appropriate in vitro and in vivo models to mimic disease, and with technologies that enable accurate monitoring, imaging and quantification of the ECM.

Algorithms for ultrasound elastography: a survey

Elastography is a useful and interesting technique that can be used to infer stiffness information from ultrasound medical images. In one decade of activity, the scientific community has developed this technique to a more and more mature stage, such that it has evolved into a fruitful application for clinical practice. During this decade, the evolution has proceeded from qualitative stiffness information to numerical quantification using different algorithms and approaches, based on both iterative and direct approaches. Moreover, different post- and pre-processing techniques as well as evaluation methods have been implemented. This work presents a survey on the methods developed by giving a short overview of algorithms and mathematical solutions and by analysing results and comparing methodologies.

Integrating active shape models into ultrasound elastography to diagnose musculoskeletal injuries: a 2D simulation study

We build on ultrasound elastography (UE) by offering a new method for diagnosing musculoskeletal injuries from estimated tissue displacements. Our strategy is to isolate the portion of tissue displacements that arise due to injury. Active shape models are constructed capturing displacement variation among normal tissue. New tissue is then evaluated by estimating displacements with (1) the active shape models and (2) a traditional UE tracking algorithm. The difference between the two estimates defined virtual axial displacement and used to identify injured tissue. Our method was tested by simulating planar tissue examined with ultrasound elastography. Images are presented of axial displacement and virtual axial displacement as well as axial strain and virtual axial strain, i.e. partial derivative of the respective displacements with respect to the axial coordinate. Injured tissue and uninjured tissue were not statistically different when comparing mean absolute value of axial strain covarying with the loading conditions. In contrast, uninjured tissue and injured tissue were statistically different when comparing absolute value of virtual axial strain covarying with loading conditions (p < 0.0001). Statistical significance was considered p < 0.05.