A Spline Interpolation-based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography.

Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young's modulus, Poisson's ratio, interstitial permeability, and vascular permeability. To estimate interstitial permeability and vascular permeability using poroelastography, accurate estimation of the strain time constant (TC) is required. This can be a challenging task due to the nonlinearity of the exponential strain curve and noise affecting the experimental data. Due to motion artifacts caused by the sonographer, animal/patient, and/or the environment, noise affecting some strain frames can be significantly higher than the strain signal. If these frames are used for the computation of the strain TC, the resulting TC estimate can be highly inaccurate, which, in turn, can cause high errors in the reconstructed mechanical parameters. In this paper, we introduce a cubic spline-based interpolation method, which allows to use only good quality strain frames (i.e., frames with sufficiently high signal-to-noise ratio [SNR]) to estimate the strain TC. Using finite element simulations, we demonstrate that the proposed interpolation method can improve the estimation accuracy of the strain TC by 46% with respect to the case where no interpolation and filtering are used and by 37% with respect to the case where the strain frames are Kalman filtered before TC estimation (at an SNR of 30 dB). We also prove the technical feasibility of the proposed technique using in vivo experimental data. While detecting the bad frames in both simulations and experiments, we assumed the lower limit SNR to be below 10 dB. Based on our results, the proposed technique may be of great help in applications relying on the accurate assessment of the temporal behavior of strain data.

Ultrasound shear wave elastography effectively predicts integrity of ventral hernia repair using acellular dermal matrix augmented with platelet-rich plasma (PRP).

BACKGROUND: Complications of ventral hernia repair (VHR) may be investigated by computed tomography or ultrasound (US) but neither modality gives a quantifiable metric of repair quality short of identifying hernia recurrence. Platelet-rich plasma (PRP), a growth factor-rich autologous blood product, has been shown to improve incorporation of native tissue with bioprosthetics. In this study, we investigate the effect of PRP on the incorporation and mechanical integrity of a non-crosslinked porcine acellular dermal matrix (pADM) in a rodent model of VHR and the correlative ability of ultrasound shear wave elastography (US-SWE) to assess repair quality. METHODS: PRP was isolated from whole blood of Lewis rats. Twenty-eight Lewis rats underwent chronic VHR using either pADM alone or augmented with autologous PRP prior to non-invasive imaging assessment and specimen harvest at either 3 or 6 months. US-SWE was performed to estimate the Young's modulus prior to histological assessment and data from PRP-treated rodents were compared to controls. RESULTS: Implanted pADM was easily distinguishable by US-SWE imaging in all cases analyzed in this study. The mean Young's modulus measured was 1.78 times and 2.54 times higher in PRP-treated samples versus control at 3-month and 6-month time points respectively (p < 0.05). At 3 months, qualitative and quantitative histology revealed decreased inflammation and improved incorporation in PRP-treated samples along the implant/abdominal wall interface. At 6 months, the PRP cohort had no hernia recurrence and preserved ADM integrity from immunologic degradation, while all control animals suffered hernia recurrence (4/6) or extreme ADM thinning (2/6). CONCLUSION: This study confirms both the efficacy of PRP in augmenting VHR using pADM, as well as the reliability of US-SWE to non-invasively predict the quality of VHR. Although further human studies are necessary, this work supports PRP use to improve VHR outcomes and US-SWE potential for bedside non-invasive hernia characterization.

An Analytical Poroelastic Model of a Nonhomogeneous Medium Under Creep Compression for Ultrasound Poroelastography Applications-Part II.

An analytical theory for the unconfined creep behavior of a cylindrical inclusion (simulating a soft tissue tumor) embedded in a cylindrical background sample (simulating normal tissue) is presented and analyzed in this paper. Both the inclusion and the background are considered as fluid-filled, porous materials, each of them being characterized by a set of mechanical parameters. Specifically, in this derivation, the inclusion is assumed to have significantly higher interstitial permeability than the background. The formulations of the effective Poisson's ratio (EPR) and fluid pressure in the inclusion and in the background are derived for the case of a sample subjected to a creep compression. The developed analytical expressions are validated using finite element models (FEM). Statistical comparison between the results obtained from the developed model and the results from FEM demonstrates accuracy of the proposed theoretical model higher than 99.4%. The model presented in this paper complements the one reported in the companion paper (Part I), which refers to the case of an inclusion having less interstitial permeability than the background.

An Analytical Poroelastic Model of a Nonhomogeneous Medium Under Creep Compression for Ultrasound Poroelastography Applications-Part I.

An analytical theory for the unconfined creep behavior of a cylindrical inclusion (simulating a soft tissue tumor) embedded in a cylindrical background sample (simulating normal tissue) is presented and analyzed in this paper. Both the inclusion and the background are considered as fluid-filled, porous materials, each of them being characterized by a set of mechanical properties. Specifically, in this paper, the inclusion is considered to be less permeable than the background. The cylindrical sample is compressed using a constant pressure within two frictionless plates and is allowed to expand in an unconfined way along the radial direction. Analytical expressions for the effective Poisson's ratio (EPR) and fluid pressure inside and outside the inclusion are derived and analyzed. The theoretical results are validated using finite element models (FEMs). Statistical analysis shows excellent agreement between the results obtained from the developed model and the results from FEM. Thus, the developed theoretical model can be used in medical imaging modalities such as ultrasound poroelastography to extract the mechanical parameters of tissues and/or to better understand the impact of different mechanical parameters on the estimated displacements, strains, stresses, and fluid pressure inside a tumor and in the surrounding tissue.

Characterization of ventral incisional hernia and repair using shear wave elastography.

BACKGROUND: To assess the integrity of hernia repair, imaging modalities such as computed tomography or ultrasound (US) are commonly used. Neither modality has currently the capacity to simultaneously image the mesh and quantify a prosthetic and surrounding tissue stiffness. In this pilot study, we hypothesize that US shear wave elastography (SWE) can be used to identify a polyester mesh and a biologic graft and to assess their stiffness noninvasively in a rat model of bridging hernia repair. METHODS: Lewis rats underwent hernia creation and repair with Parietex or Strattice at 30 d. After 3 mo, the animals were euthanized, and the Young's Modulus was measured using SWE. Three-dimensional reconstructions of the hernia pre- and post-repair were performed using in-house image processing algorithms. RESULTS: SWE was capable of accurate and real-time assessment and diagnosis of the hernia defects in vivo. Young's Modulus of Parietex meshes and Strattice grafts as estimated from the shear wave elastograms were found to be statistically different from each other (P < 0.05). Accurate three-dimensional reconstructions of the hernia defects pre- and post-repair were generated. CONCLUSIONS: In this study, we demonstrate the feasibility of using US SWE to detect ventral hernias and evaluate mesh repair in vivo. Our results indicate that the presence of a hernia and repair can be reliably visualized by SWE and three dimensionally reconstructed. Thus, this technique may provide both structural and functional information regarding the hernia and the repair.

Effect of Interstitial Fluid Pressure on Ultrasound Axial Strain and Axial Shear Strain Elastography.

Ultrasound elastography is an imaging modality that has been used to diagnose tumors of the breast, thyroid, and prostate. Both axial strain elastography and axial shear strain elastography (ASSE) have shown significant potentials to differentiate between benign and malignant tumors. Elevated interstitial fluid pressure (IFP) is a characteristic of many malignant tumors and a major barrier in targeted drug delivery therapies. This parameter, however, has not received significant attention in ultrasound elastography and, in general, in most diagnostic imaging modalities yet. In this paper, we investigate the effect of an underlying IFP contrast on ultrasound axial strain and axial shear strain imaging using finite element analysis. Our results show that an underlying contrast in IFP creates a new contrast mechanism in both the axial strain and axial shear strain elastographic images. This information might be important for a better interpretation of elastographic images of tumors.

Effect of temporal acquisition parameters on image quality of strain time constant elastography.

Ultrasound methods to image the time constant (TC) of elastographic tissue parameters have been recently developed. Elastographic TC images from creep or stress relaxation tests have been shown to provide information on the viscoelastic and poroelastic behavior of tissues. However, the effect of temporal ultrasonic acquisition parameters and input noise on the image quality of the resultant strain TC elastograms has not been fully investigated yet. Understanding such effects could have important implications for clinical applications of these novel techniques. This work reports a simulation study aimed at investigating the effects of varying windows of observation, acquisition frame rate, and strain signal-to-noise ratio (SNR) on the image quality of elastographic TC estimates. A pilot experimental study was used to corroborate the simulation results in specific testing conditions. The results of this work suggest that the total acquisition time necessary for accurate strain TC estimates has a linear dependence to the underlying strain TC (as estimated from the theoretical strain-vs.-time curve). The results also indicate that it might be possible to make accurate estimates of the elastographic TC (within 10% error) using windows of observation as small as 20% of the underlying TC, provided sufficiently fast acquisition rates (>100 Hz for typical acquisition depths). The limited experimental data reported in this study statistically confirm the simulation trends, proving that the proposed model can be used as upper bound guidance for the correct execution of the experiments.

Non-invasive imaging of mechanical properties of cancers in vivo based on transformations of the Eshelby's tensor using compression elastography.

Knowledge of the mechanical properties is of great clinical significance for diagnosis, prognosis and treatment of cancers. Recently, a new method based on Eshelby's theory to simultaneously assess Young's modulus (YM) and Poisson's ratio (PR) in tissues has been proposed. A significant limitation of this method is that accuracy of the reconstructed YM and PR is affected by the orientation/alignment of the tumor with the applied stress. In this paper, we propose a new method to reconstruct YM and PR in cancers that is invariant to the 3D orientation of the tumor with respect to the axis of applied stress. The novelty of the proposed method resides on the use of a tensor transformation to improve the robustness of Eshelby's theory and reconstruct YM and PR of tumors with high accuracy and in realistic experimental conditions. The method is validated using finite element simulations and controlled experiments using phantoms with known mechanical properties. The in vivo feasibility of the developed method is demonstrated in an orthotopic mouse model of breast cancer. Our results show that the proposed technique can estimate the YM and PR with overall accuracy of (97.06 ± 2.42) % under all tested tumor orientations. Animal experimental data demonstrate the potential of the proposed methodology in vivo. The proposed method can significantly expand the range of applicability of the Eshelby's theory to tumors and provide new means to accurately image and quantify mechanical parameters of cancers in clinical conditions.

Enhancing ECG-based heart age: impact of acquisition parameters and generalization strategies for varying signal morphologies and corruptions.

Electrocardiogram (ECG) is a non-invasive approach to capture the overall electrical activity produced by the contraction and relaxation of the cardiac muscles. It has been established in the literature that the difference between ECG-derived age and chronological age represents a general measure of cardiovascular health. Elevated ECG-derived age strongly correlates with cardiovascular conditions (e.g., atherosclerotic cardiovascular disease). However, the neural networks for ECG age estimation are yet to be thoroughly evaluated from the perspective of ECG acquisition parameters. Additionally, deep learning systems for ECG analysis encounter challenges in generalizing across diverse ECG morphologies in various ethnic groups and are susceptible to errors with signals that exhibit random or systematic distortions To address these challenges, we perform a comprehensive empirical study to determine the threshold for the sampling rate and duration of ECG signals while considering their impact on the computational cost of the neural networks. To tackle the concern of ECG waveform variability in different populations, we evaluate the feasibility of utilizing pre-trained and fine-tuned networks to estimate ECG age in different ethnic groups. Additionally, we empirically demonstrate that finetuning is an environmentally sustainable way to train neural networks, and it significantly decreases the ECG instances required (by more than 100 × ) for attaining performance similar to the networks trained from random weight initialization on a complete dataset. Finally, we systematically evaluate augmentation schemes for ECG signals in the context of age estimation and introduce a random cropping scheme that provides best-in-class performance while using shorter-duration ECG signals. The results also show that random cropping enables the networks to perform well with systematic and random ECG signal corruptions.

Non-invasive imaging of interstitial fluid transport parameters in solid tumors in vivo.

In this paper, new and non-invasive imaging methods to assess interstitial fluid transport parameters in tumors in vivo are developed, analyzed and experimentally validated. These parameters include extracellular volume fraction (EVF), interstitial fluid volume fraction (IFVF) and interstitial hydraulic conductivity (IHC), and they are known to have a critical role in cancer progression and drug delivery effectiveness. EVF is defined as the volume of extracellular matrix per unit volume of the tumor, while IFVF refers to the volume of interstitial fluid per unit bulk volume of the tumor. There are currently no established imaging methods to assess interstitial fluid transport parameters in cancers in vivo. We develop and test new theoretical models and imaging techniques to assess fluid transport parameters in cancers using non-invasive ultrasound methods. EVF is estimated via the composite/mixture theory with the tumor being modeled as a biphasic (cellular phase and extracellular phase) composite material. IFVF is estimated by modeling the tumor as a biphasic poroelastic material with fully saturated solid phase. Finally, IHC is estimated from IFVF using the well-known Kozeny-Carman method inspired by soil mechanics theory. The proposed methods are tested using both controlled experiments and in vivo experiments on cancers. The controlled experiments were performed on tissue mimic polyacrylamide samples and validated using scanning electron microscopy (SEM). In vivo applicability of the proposed methods was demonstrated using a breast cancer model implanted in mice. Based on the controlled experimental validation, the proposed methods can estimate interstitial fluid transport parameters with an error below 10% with respect to benchmark SEM data. In vivo results demonstrate that EVF, IFVF and IHC increase in untreated tumors whereas these parameters are observed to decrease over time in treated tumors. The proposed non-invasive imaging methods may provide new and cost-effective diagnostic and prognostic tools to assess clinically relevant fluid transport parameters in cancers in vivo.

Predicting stress and interstitial fluid pressure in tumors based on biphasic theory.

The uncontrolled proliferation of cancer cells causes the growth of the tumor mass. Consequently, the normal surrounding tissue exerts a compressive force on the tumor mass to oppose its expansion. These stresses directly promote tumor metastasis and invasion and affect drug delivery. In the past, the mechanical behavior of solid tumors has been extensively studied using linear elastic and nonlinear hyperelastic constitutive models. In this study, we develop a two-dimensional biomechanical model based on the biphasic assumption of the solid matrix and fluid phase of the tissues. Heterogeneous vasculature and nonuniform blood perfusion are also investigated by incorporating in the model a necrotic core and a well-vascularized zone. The findings of our study demonstrate a significant difference between the linear and nonlinear tissue responses to stress, while the interstitial fluid pressure (IFP) distribution is found to be independent of the constitutive model. The proposed biphasic model may be useful for elasticity imaging techniques aiming at predicting stress and IFP in tumors.

Assessment of compression-induced solid stress, fluid pressure and mechanopathological parameters in cancersin vivousing poroelastography.

Objective.Compression-induced solid stress (SSc) and fluid pressure (FPc) during ultrasound poroelastography (USPE) experiments are correlated with two markers of cancer growth and treatment effectiveness: growth-induced solid stress (SSg) and interstitial fluid pressure (IFP). The spatio-temporal distributions of SSg and IFP are determined by the transport properties of the vessels and interstitium in the tumor microenvironment.Approach.We propose a new USPE method for the non-invasive imaging of the local cancer mechanical parameters and dynamics of fluid flow. When performing poroelastography experiments, it may be difficult to implement a typical creep compression protocol, which requires to maintain a constant normally applied force. In this paper, we investigate the use of a stress relaxation protocol, which might be a more convenient choice for clinical poroelastography applications.Main results.Based on our finite element and ultrasound simulations study, we demonstrate that the SSc, FPc and their spatio-temporal distribution related parameters, interstitial permeability and vascular permeability, can be determined from stress relaxation experiments with errors below 10% as compared to the ground truth and accuracy similar to that of corresponding creep tests, respectively. We also demonstrate the feasibility of the new methodology forin vivoexperiments using a small animal cancer model.Significance.The proposed non-invasive USPE imaging methods may become an effective tool to assess local tumor pressure and mechanopathological parameters in cancers.

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound.

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade.

Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.

Assessment of spinal cord injury using ultrasound elastography in a rabbit model in vivo.

The effect of the mechanical micro-environment on spinal cord injury (SCI) and treatment effectiveness remains unclear. Currently, there are limited imaging methods that can directly assess the localized mechanical behavior of spinal cords in vivo. In this study, we apply new ultrasound elastography (USE) techniques to assess SCI in vivo at the site of the injury and at the time of one week post injury, in a rabbit animal model. Eleven rabbits underwent laminectomy procedures. Among them, spinal cords of five rabbits were injured during the procedure. The other six rabbits were used as control. Two neurological statuses were achieved: non-paralysis and paralysis. Ultrasound data were collected one week post-surgery and processed to compute strain ratios. Histologic analysis, mechanical testing, magnetic resonance imaging (MRI), computerized tomography and MRI diffusion tensor imaging (DTI) were performed to validate USE results. Strain ratios computed via USE were found to be significantly different in paralyzed versus non-paralyzed rabbits. The myelomalacia histologic score and spinal cord Young's modulus evaluated in selected animals were in good qualitative agreement with USE assessment. It is feasible to use USE to assess changes in the spinal cord of the presented animal model. In the future, with more experimental data available, USE may provide new quantitative tools for improving SCI diagnosis and prognosis.

Ultrasound estimation of strain time constant and vascular permeability in tumors using a CEEMDAN and linear regression-based method.

Ultrasound poroelastography focuses on the estimation of the spatio-temporal mechanical behavior of tissues using data often corrupted with non-stationary noise. The cumulative strain calculated from prolonged temporal acquisition of RF data can face the problem of aggregate noise. This noise can significantly affect the accuracy of curve fitting techniques necessary to estimate the clinically significant strain Time Constant (TC) and related parameters. We present a new technique, which decomposes the non-linear temporal behavior of the differential strain to extract the monotonic decaying trend by using the time-domain and data-driven Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. A linear regression scheme is then used to obtain the slope of the transformed non-linear trend, which carries information about the strain TC. Assessment of Vascular Permeability (VP), a transport parameter indicative of tumor growth, requires accurate strain TC estimations. Finite Element (FE), ultrasound simulations and in vivo experiments are used to investigate the performance of the proposed technique. Based on the simulation analysis, the average Percentage Relative Error (PRE) values of our method are 4.15% (for TC estimation) and 5.00% (for VP estimation) at 20 dB SNR level for different Percentage of Good Frames (PGF) (i.e., 20%, 50%, 75%, and 100%). These PRE values are substantially lower than those obtained using other conventional elastographic techniques. Our proposed method could become a new data-adaptive tool for analyzing the non-linear time-dependent response of complex tissues such as cancers.

Estimation of Mechanical and Transport Parameters in Cancers Using Short Time Poroelastography.

Mechanical and transport properties of cancers such as Young's modulus (YM), Poisson's ratio (PR), and vascular permeability (VP) have great clinical importance in cancer diagnosis, prognosis, and treatment. However, non-invasive estimation of these parameters in vivo is challenged by many practical factors. Elasticity imaging methods, such as "poroelastography", require prolonged data acquisition, which can limit their clinical applicability. In this paper, we investigate a new method to perform poroelastography experiments, which results in shorter temporal acquisition windows. This method is referred to as "short-time poroelastography" (STPE). Finite element (FE) and ultrasound simulations demonstrate that, using STPE, it is possible to accurately estimate YM, PR (within 10% error) using windows of observation (WoOs) of length as short as 1 underlying strain Time Constant (TC). The error was found to be almost negligible (< 3%) when using WoOs longer than 2 strain TCs. In the case of VP estimation, WoOs of at least 2 strain TCs are required to obtain an error < 8% (in simulations). The stricter requirement for the estimation of VP with respect to YM and PR is due its reliance on the transient strain behavior while YM and PR depend on the steady state strain values only. In vivo experimental data are used as a proof-of-principle of the potential applicability of the proposed methodology in vivo. The use of STPE may provide a means to efficiently perform poroelastography experiments without compromising the accuracy of the estimated tissue properties.

A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo.

Three-dimensional (3-D) reconstruction of the spine surface is of strong clinical relevance for the diagnosis and prognosis of spine disorders and intra-operative image guidance. In this paper, we report a new technique to reconstruct lumbar spine surfaces in 3-D from non-invasive ultrasound (US) images acquired in free-hand mode. US images randomly sampled from in vivo scans of 9 rabbits were used to train a U-net convolutional neural network (CNN). More specifically, a late fusion (LF)-based U-net trained jointly on B-mode and shadow-enhanced B-mode images was generated by fusing two individual U-nets and expanding the set of trainable parameters to around twice the capacity of a basic U-net. This U-net was then applied to predict spine surface labels in in vivo images obtained from another rabbit, which were then used for 3-D spine surface reconstruction. The underlying pose of the transducer during the scan was estimated by registering stacks of US images to a geometrical model derived from corresponding CT data and used to align detected surface points. Final performance of the reconstruction method was assessed by computing the mean absolute error (MAE) between pairs of spine surface points detected from US and CT and by counting the total number of surface points detected from US. Comparison was made between the LF-based U-net and a previously developed phase symmetry (PS)-based method. Using the LF-based U-net, the averaged number of US surface points across the lumbar region increased by 21.61% and MAE reduced by 26.28% relative to the PS-based method. The overall MAE (in mm) was 0.24±0.29. Based on these results, we conclude that: 1) the proposed U-net can detect the spine posterior arch with low MAE and large number of US surface points and 2) the newly proposed reconstruction framework may complement and, under certain circumstances, be used without the aid of an external tracking system in intra-operative spine applications.

Identification of ultrasound imaging markers to quantify long bone regeneration in a segmental tibial defect sheep model in vivo.

The healing of large bone defects has been investigated for decades due to its complexity and clinical relevance. Ultrasound (US) methods have shown promise in monitoring bone healing, but no quantitative method to assess regenerated bone morphology in US images has been presented yet. In this study, we investigate new US morphometric parameters to quantify bone regeneration in vivo. A segmental tibial defect was surgically created and stabilized in a sheep animal model. US and computed tomography (CT) imaging data were collected two months post-surgery. New bone was assessed, reconstructed and quantified from the US and CT data using 3 morphometric parameters: the new-bone bulk (NBB), new-bone surface (NBS) and new-bone contact (NBC). The distance (mm) between surface reconstructions from repeated US was [Formula: see text] and from US and CT was [Formula: see text]. In the mid-shaft of the defected tibia, US measurements of NBB, NBS and NBC were significantly higher than the corresponding CT measurements ([Formula: see text]). Based on our results, we conclude that US may complement CT to reconstruct and quantify bone regrowth, especially in its early stages.

Estimation of Vascular Permeability in Irregularly Shaped Cancers Using Ultrasound Poroelastography.

OBJECTIVE: Vascular permeability (VP) is a mechanical parameter which plays an important role in cancer initiation, metastasis, and progression. To date, there are only a few non-invasive methods that can be used to image VP in solid tumors. Most of these methods require the use of contrast agents and are expensive, limiting widespread use. METHODS: In this paper, we propose a new method to image VP in tumors, which is based on the use of ultrasound poroelastography. Estimation of VP by poroelastography requires knowledge of the Young's modulus (YM), Poisson's ratio (PR), and strain time constant (TC) in the tumors. In our method, we find the ellipse which best fits the tumor (regardless of its shape) using an eigen-system-based fitting technique and estimate the YM and PR using Eshelby's elliptic inclusion formulation. A Fourier method is used to estimate the axial strain TC, which does not require any initial guess and is highly robust to noise. RESULTS: It is demonstrated that the proposed method can estimate VP in irregularly shaped tumors with an accuracy of above [Formula: see text] using ultrasound simulation data with signal-to-noise ratio of 20 dB or higher. In vivo feasibility of the proposed technique is demonstrated in an orthotopic mouse model of breast cancer. CONCLUSION: The proposed imaging method can provide accurate and localized estimation of VP in cancers non-invasively and cost-effectively. SIGNIFICANCE: Accurate and non-invasive assessment of VP can have a significant impact on diagnosis, prognosis, and treatment of cancers.