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Purpose: An enhanced ultrasound elastography technique is proposed for early assessment of locally advanced breast cancer (LABC) response to neoadjuvant chemotherapy (NAC)., Methods: The proposed elastography technique inputs ultrasound radiofrequency data obtained through tissue quasi-static stimulation and adapts a strain refinement algorithm formulated based on fundamental principles of continuum mechanics, coupled with an iterative inverse finite element method to reconstruct the breast Young's modulus (E) images. The technique was explored for therapy response assessment using data acquired from 25 LABC patients before and at weeks 1, 2, and 4 after the NAC initiation (100 scans). The E ratio of tumor to the surrounding tissue was calculated at different scans and compared to the baseline for each patient. Patients' response to NAC was determined many months later using standard clinical and histopathological criteria., Results: Reconstructed E ratio changes obtained as early as one week after the NAC onset demonstrate very good separation between the two cohorts of responders and non-responders to NAC. Statistically significant differences were observed in the E ratio changes between the two patient cohorts at weeks 1 to 4 after treatment (p-value < 0.001; statistical power greater than 97%). A significant difference in axial strain ratio changes was observed only at week 4 (p-value = 0.01; statistical power = 76%). No significant difference was observed in tumor size changes at weeks 1, 2 or 4., Conclusion: The proposed elastography technique demonstrates a high potential for chemotherapy response monitoring in LABC patients and superior performance compared to strain imaging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction., Methods: The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases., Results: The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images., Conclusion: The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic., (© 2022 American Association of Physicists in Medicine.)

Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this paper, we show the effectiveness of an enhanced method of quasi-static ultrasound elastography for liver cancer assessment. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The strain refinement algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we use strain images generated by the proposed method with an iterative elasticity reconstruction algorithm. Ultrasound RF data collected from a tissue-mimicking phantom and in-vivo data of a liver cancer patient were used to evaluate the proposed method. Results show that while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates. Clinical Relevance- Improved elasticity images of the liver can aid in achieving more reliable diagnosis of liver cancer.

Purpose: The aortic time-enhancement curve obtained from dynamic CT myocardial perfusion imaging can be used to derive the cardiac output (CO) index based on the indicator dilution principle. The objective of this study was to investigate the effect of cardiac phase at which CT myocardial perfusion imaging is triggered on the CO index measurement with this approach. Methods: Electrocardiogram (ECG) gated myocardial perfusion imaging was performed on farm pigs with consecutive cardiac axial scans using a large-coverage CT scanner (Revolution, GE Healthcare) after intravenous contrast administration. Multiple sets of dynamic contrast-enhanced (DCE) cardiac images were reconstructed retrospectively from 30% to 80% R-R intervals with a 5% phase increment. The time-enhancement curve sampled from above the aortic orifice in each DCE image set was fitted with a modified gamma variate function (MGVF). The fitted curve was then normalized to the baseline data point unaffected by the streak artifact emanating from the contrast solution in the right heart chamber. The Stewart−Hamilton equation was used to calculate the CO index based on the integral of the fitted normalized aortic curve, and the results were compared among different cardiac phases. Results: The aortic time-enhancement curves sampled at different cardiac phases were different from each other, especially in the baseline portion of the curve where the effect of streak artifact was prominent. After properly normalizing and denoising with a MGVF, the integrals of the aortic curve were minimally different among cardiac phases (0.228 ± 0.001 Hounsfield Unit × second). The corresponding mean CO index was 4.031 ± 0.028 L/min. There were no statistical differences in either the integral of the aortic curve or CO index among different cardiac phases (p > 0.05 for all phases).

The mechanical properties of normal soft tissues, including breast tissue, have been of interest to the biomedical research community as there are many clinical and industrial applications that can benefit from quantitative information characterizing such properties. For instance, computer assisted surgery planning, elastography for breast cancer diagnosis, and bra design can all involve biomechanical modeling of the breast to predict its deformation or stress distribution. It is known that most biological soft tissues, including breast tissue, exhibit nonlinear mechanical response over large strains. As such, it is necessary to model such tissues as hyperelastic. In this work, we used indentation testing to estimate the hyperelastic parameters of 4 models (3rd order Ogden, 5-term polynomial, Veronda-Westman and Yeoh) estimated from 72 healthy ex vivo breast tissue samples covering adipose, fibroglandular, and mixed tissue. All estimated parameter sets were confirmed to represent stable material using Drucker's stability criterion. We observed that all three tissue types were statistically similar solidifying the use of homogenous breast modelling over large strain simulation., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Ultrasound elastography is a prominent noninvasive medical imaging technique that estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, the edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique: Second-Order Ultrasound eLastography (SOUL). Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE, and MPWC-Net++. SOUL yields 27.72%, 62.56%, and 81.37% improvements of the signal-to-noise ratio (SNR) and 72.35%, 54.03%, and 65.17% improvements of the contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom, and in vivo tissue, respectively. The SOUL code can be downloaded from code.sonography.ai.

Medical imaging derived cardiac biomechanical models offer a wealth of new information to be used in diagnosis and prognosis of cardiovascular disease. A noteworthy feature of such models is the ability to predict myofiber contraction stresses during acute or chronic ischemic events. Current techniques for heterogeneous contraction models require tissue motion tracking capabilities which are neither available on all imaging modalities, nor currently used in the clinic. Proposed in this article is a proof of concept of a tissue tracking independent technique focused on shape optimization to predict the contraction stresses of in-silico left ventricle models simulating various acute myocardial infarction events. The technique involves three variables defined in the left ventricle muscle. Two of the variables represent the contraction stresses in the healthy and infarct regions while the third is a novel periinfarct variable defining a non-contracting myofiber state allowing finer classification of local myofiber damage. Results indicate that the contraction stress reconstruction errors are overall smaller than 12% when considering standard errors associated with population modelling for the new variable of interest., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Dual-modality 4D cardiac data visualization can convey a significant amount of complementary image information from various sources into a single and meaningful display. Even though there are existing publications on combining multiple medical images into a unique representation, there has been no work on rendering a series of cardiac image sequences, acquired from multiple sources, using web browsers and synchronizing the result over the Internet in real time. The ability to display multi-modality beating heart images using Web-based technology is hampered by the lack of efficient algorithms for fusing and visualizing constantly updated multi-source images and streaming the rendering results using internet protocols. To address this practical issue, in this paper we introduce a new Internet-based algorithm and a software platform running on a Node.js server, where a series of registered cardiac images from both magnetic resonance (MR) and ultrasound are employed to display dynamic fused cardiac structures in web browsers. Taking advantage of the bidirectional WebSocket protocol and WebGL-based graphics acceleration, internal cardiac structures are dynamically displayed, and the results of rendering and data exploration are synchronized among all the connected client computers. The presented research and software have the potential to provide clinicians with comprehensive information and intuitive feedback relating to cardiac behavior and anatomy and could impact areas such as distributed diagnosis of cardiac function and collaborative treatment planning for various heart diseases., (Copyright © 2020. Published by Elsevier Ltd.)

There is growing awareness that brain mechanical properties are important for neural development and health. However, published values of brain stiffness differ by orders of magnitude between static measurements and in vivo magnetic resonance elastography (MRE), which covers a dynamic range over several frequency decades. We here show that there is no fundamental disparity between static mechanical tests and in vivo MRE when considering large-scale properties, which encompass the entire brain including fluid filled compartments. Using gradient echo real-time MRE, we investigated the viscoelastic dispersion of the human brain in, so far, unexplored dynamic ranges from intrinsic brain pulsations at 1 Hz to ultralow-frequency vibrations at 5, 6.25, 7.8 and 10 Hz to the normal frequency range of MRE of 40 Hz. Surprisingly, we observed variations in brain stiffness over more than two orders of magnitude, suggesting that the in vivo human brain is superviscous on large scales with very low shear modulus of 42±13 Pa and relatively high viscosity of 6.6±0.3 Pa∙s according to the two-parameter solid model. Our data shed light on the crucial role of fluid compartments including blood vessels and cerebrospinal fluid (CSF) for whole brain properties and provide, for the first time, an explanation for the variability of the mechanical brain responses to manual palpation, local indentation, and high-dynamic tissue stimulation as used in elastography., Competing Interests: Declaration of Competing Interest The authors declare no potential conflicts of interest with respect to the research, authorship, and publication of this article., (Copyright © 2020. Published by Elsevier Ltd.)

Menisci are fibrocartilaginous disks consisting of soft tissue with a complex biomechanical structure. They are critical determinants of the kinematics as well as the stability of the knee joint. Several studies have been carried out to formulate tissue mechanical behavior, leading to the development of a wide spectrum of constitutive laws. In addition to developing analytical tools, extensive numerical studies have been conducted on menisci modeling. This study reviews the developments of the most widely used continuum models of the meniscus mechanical properties in conjunction with emerging analytical and numerical models used to study the meniscus. The review presents relevant approaches and assumptions used to develop the models and includes discussions regarding strengths, weaknesses, and discrepancies involved in the presented models. The study presents a comprehensive coverage of relevant publications included in Compendex, EMBASE, MEDLINE, PubMed, ScienceDirect, Springer, and Scopus databases. This review aims at opening novel avenues for improving menisci modeling within the framework of constitutive modeling through highlighting the needs for further research directed toward determining key factors in gaining insight into the biomechanics of menisci which is crucial for the elaborate design of meniscal replacements.

Patient-specific finite element (FE) modeling of the upper airway is an effective tool for accurate assessment of obstructive sleep apnea (OSA) syndrome. It is also useful for planning minimally invasive surgical procedures under severe OSA conditions. A major requirement of FE modeling is having reliable data characterizing the biomechanical properties of the upper airway tissues, particularly oropharyngeal soft tissue. While some data characterizing this tissue's linear elastic regime is available, reliable data characterizing its hyperelasticity is scarce. The aim of the current study is to estimate the hyperelastic mechanical properties of the oropharyngeal soft tissues, including the palatine tonsil, soft palate, uvula, and tongue base. Fresh tissue specimens of human oropharyngeal tissue were acquired from 13 OSA patients who underwent standard surgical procedures. Indentation testing was performed on the specimens to obtain their force-displacement data. To determine the specimens' hyperelastic parameters using these data, an inverse FE framework was utilized. In this work, the hyperelastic parameters corresponding to the commonly used Yeoh and 2nd order Ogden models were obtained. Both models captured the experimental force-displacement data of the tissue specimens reasonably accurately with mean errors of 11.65% or smaller. This study has provided estimates of the hyperelastic parameters of all upper airway soft tissues using fresh human tissue specimens for the first time., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety of cardiovascular diseases and injuries. In order to have an accurate biomechanical model, personalized parameters to define loading, boundary conditions and mechanical properties are required. Achieving personalized modelling parameters often requires inverse optimization which is computationally expensive; hence techniques to reduce the multivariable complexity are in need. Presented in this paper is the fundamental blueprint to create a library of scar tissue mechanical properties to be used in modelling the healing mechanics of hearts that have suffered acute myocardial infarction. This library can be used to reduce the number of variables necessary to capture the scar tissue mechanical properties down to 1. This single parameter also carries information pertaining to staging of the scar tissue healing, predict its rate, and predict its collagen density. This information can be potentially used as valuable biomarkers to adjust existing or develop new treatment plans for patients.

Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.

Low dose computed tomography (LDCT) is the current gold-standard for lung cancer diagnosis. However, accuracy of diagnosis is limited by the radiologist's ability to discern cancerous from non-cancerous nodules. To assist with diagnoses, a 4D-CT lung elastography method is proposed to distinguish nodules based on tissue stiffness properties. The technique relies on a patient-specific inverse finite element (FE) model of the lung solved using an optimization algorithm. The FE model incorporates hyperelastic material properties for tumor and healthy regions and was deformed according to respiration physiology. The tumor hyperelastic parameters and trans-pulmonary pressure were estimated using an optimization algorithm that maximizes similarity between the actual and simulated tumor and lung image data. The proposed technique was evaluated using an in-silico study where the lung tumor elastic properties were assumed. Following that evaluation, the technique was applied to clinical 4D-CT data of two lung cancer patients. Results from the evaluation study show that the elastography technique recovered known tumor parameters with only 6% error. Tumor hyperelastic properties from the clinical data are also reported. Results from this proof of concept study demonstrate the ability to perform lung elastography with 4D-CT data alone. Advancements in the technique could lead to improved diagnoses and timely treatment of lung cancer.

Many types of cancers are associated with changes in tissue mechanical properties. This has led to the development of elastography as a clinically viable method where tissue mechanical properties are mapped and visualized for cancer detection and staging. In quasi-static ultrasound elastography, a mechanical stimulation is applied to the tissue using ultrasound probe. Using ultrasound radiofrequency (RF) data acquired before and after the stimulation, the tissue displacement field can be estimated. Elasticity image reconstruction algorithms use this displacement data to generate images of the tissue elasticity properties. The accuracy of the generated elasticity images depends highly on the accuracy of the tissue displacement estimation. Tissue incompressibility can be used as a constraint to improve the estimation of axial and, more importantly, the lateral displacements in 2D ultrasound elastography. Especially in clinical applications, this requires accurate estimation of the out-of-plane strain. Here, we propose a method for providing an accurate estimate of the out-of-plane strain which is incorporated in the incompressibility equation to improve the axial and lateral displacements estimation before elastography image reconstruction. The method was validated using in silico and tissue mimicking phantom studies, leading to significant improvement in the estimated displacement.

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.

Radiation therapy (RT) is an important component of treatment for lung cancer. However, the accuracy of this method can be affected by the complex respiratory motion/deformation of the target tumor during treatment. To improve the accuracy of RT, patient-specific biomechanical models of the lung have been proposed for estimating the tumor's respiratory motion/deformation. Chronic obstructive pulmonary disease (COPD) has a high incidence among lung cancer patients and is associated with heterogeneous destruction of lung parenchyma. This key heterogeneity element, however, has not been incorporated in lung biomechanical models developed in previous studies. In this work, we have developed a physiologically and patho-physiologically realistic lung biomechanical model that accounts for lung tissue heterogeneity. Four-dimensional computed tomography (4DCT) images were used to build a patient-specific finite element (FE) model of the lung. Image information was used to identify and incorporate inhomogeneities within the model. Mechanical properties of normal and diseased regions in the lung and the transpulmonary pressure driving the respiratory motion were estimated using an optimization algorithm that maximizes the similarity between the actual and simulated tumor and lung image data. Results from this proof of concept study on a lung cancer patient indicated improved accuracy of tumor motion estimation when COPD-induced lung tissue heterogeneities were incorporated in the model.

Damaged cardiac muscle tissue caused by ischemia leads to compromised cardiac function. While conventional imaging can view the ischemic tissue, currently there is no clinical way to quantitatively predict improved heart function after revascularization treatment. This increases the decision difficulty of treatment planning as there is no guarantee the heart function will improve enough to justify the cost of revascularization treatment. The complement of biomechanical modelling with conventional imaging offers an alternative method to determine the amount of ischemic tissue which can then be used as a potential predictor to estimate the range of functional improvement. A novel shape optimization technique is presented to predict the contractility of ischemic tissue in an in-silico left ventricle model that has suffered acute myocardial infarction. Preliminary results show that the proposed technique can reconstruct the damage caused by ischemic tissue within 18%. A range of minimum to maximum predicted cardiac improvement can then be given based on this error to help decide if the cost of revascularization treatment is justified.

Finite element (FE)-based biomechanical simulations of the upper airway are promising computational tools to study abnormal upper airway deformations under obstructive sleep apnea (OSA) conditions and to help guide minimally invasive surgical interventions in case of upper airway collapse. To this end, passive biomechanical properties of the upper airway tissues, especially oropharyngeal soft tissues, are indispensable. This research aimed at characterizing the linear elastic mechanical properties of the oropharyngeal soft tissues including palatine tonsil, soft palate, uvula, and tongue base. For this purpose, precise indentation experiments were conducted on freshly harvested human tissue samples accompanied by FE-based inversion schemes. To minimize the impact of the probable nonlinearities of the tested tissue samples, only the first quarter of the measured force-displacement data corresponding to the linear elastic regime was utilized in the FE-based inversion scheme to improve the accuracy of the tissue samples' Young's modulus calculations. Measured Young's moduli of the oropharyngeal soft tissues obtained in this study are presented. They include first estimates for palatine tonsil tissue samples while measured Young's moduli of other upper airway tissues were obtained for the first time using fresh human tissue samples., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Objective: The use of computer simulation to develop a high-fidelity model has been proposed as a novel and cost-effective alternative to help guide therapeutic intervention in sleep apnea surgery. We describe a computer model based on patient-specific anatomy of obstructive sleep apnea (OSA) subjects wherein the percentage and sites of upper airway collapse are compared to findings on drug-induced sleep endoscopy (DISE)., Study Design: Basic science computer model generation., Methods: Three-dimensional finite element techniques were undertaken for model development in a pilot study of four OSA patients. Magnetic resonance imaging was used to capture patient anatomy and software employed to outline critical anatomical structures. A finite-element mesh was applied to the volume enclosed by each structure. Linear and hyperelastic soft-tissue properties for various subsites (tonsils, uvula, soft palate, and tongue base) were derived using an inverse finite-element technique from surgical specimens. Each model underwent computer simulation to determine the degree of displacement on various structures within the upper airway, and these findings were compared to DISE exams performed on the four study patients., Results: Computer simulation predictions for percentage of airway collapse and site of maximal collapse show agreement with observed results seen on endoscopic visualization., Conclusion: Modeling the upper airway in OSA patients is feasible and holds promise in aiding patient-specific surgical treatment., Level of Evidence: NA. Laryngoscope, 128:277-282, 2018., (© 2017 The American Laryngological, Rhinological and Otological Society, Inc.)

Mechanical characterization of adipose tissue micro-components is important for various biomedical applications such as tissue engineering and predicting adipose tissue response to forces involved in relevant medical intervention procedures (e.g. breast needle biopsy). For this characterization, we introduce a novel structural method for micromechanical modeling of the adipose tissue. The micromechanical model was developed using fluid-structure interaction (FSI) formulation. We utilized this model within an inverse problem framework to estimate the hyperelastic parameters of adipose tissue extracellular matrix (ECM). Using this framework, the ECM hyperelastic parameters were changed in the FSI model systematically using an optimization algorithm such that the mechanical response obtained from the FSI model matches the corresponding experimental response reported in previous studies. To account for adipocyte size variation, the hyperelastic parameters were determined for different adipocyte sizes in the FSI model. Results obtained in this investigation indicate that at various strains under quasi-static conditions, the stiffness of adipose tissue ECM is ~ (2-3) times higher than that of the adipose tissue. The results also indicate a very good fit between the FSI model responses and their experimental counterparts. This indicates the reliability of the proposed FSI model in capturing major elements of the adipose tissue micromechanics. As such, it is potentially useful in applications such as tissue engineering, estimating tissue deformation pertaining to medical intervention and cataloging the mechanical properties of adipose tissue under health and pathological conditions. It can also be utilized as a forward model for developing inversion algorithms designed to determine pathological adipose microstructural alterations., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Computational models are effective tools to study cardiac mechanics under normal and pathological conditions. They can be used to gain insight into the physiology of the heart under these conditions while they are adaptable to computer assisted patient-specific clinical diagnosis and therapeutic procedures. Realistic cardiac mechanics models incorporate tissue active/passive response in conjunction with hyperelasticity and anisotropy. Conventional formulation of such models leads to mathematically-complex problems usually solved by custom-developed non-linear finite element (FE) codes. With a few exceptions, such codes are not available to the research community. This article describes a computational cardiac mechanics model developed such that it can be implemented using off-the-shelf FE solvers while tissue pathologies can be introduced in the model in a straight-forward manner. The model takes into account myocardial hyperelasticity, anisotropy, and active contraction forces. It follows a composite tissue modeling approach where the cardiac tissue is decomposed into two major parts: background and myofibers. The latter is modelled as rebars under initial stresses mimicking the contraction forces. The model was applied in silico to study the mechanics of infarcted left ventricle (LV) of a canine. End-systolic strain components, ejection fraction, and stress distribution attained using this LV model were compared quantitatively and qualitatively to corresponding data obtained from measurements as well as to other corresponding LV mechanics models. This comparison showed very good agreement.

A novel full-inversion-based technique for quantitative ultrasound elastography was investigated in a pilot clinical study on five patients for non-invasive detection and localization of prostate cancer and quantification of its extent. Conventional-frequency ultrasound images and radiofrequency (RF) data (~5 MHz) were collected during mechanical stimulation of the prostate using a transrectal ultrasound probe. Pre and post-compression RF data were used to construct the strain images. The Young's modulus (YM) images were subsequently reconstructed using the derived strain images and the stress distribution estimated iteratively using finite element (FE) analysis. Tumor regions determined based on the reconstructed YM images were compared to whole-mount histopathology images of radical prostatectomy specimens. Results indicated that tumors were significantly stiffer than the surrounding tissue, demonstrating a relative YM of 2.5±0.8 compared to normal prostate tissue. The YM images had a good agreement with the histopathology images in terms of tumor location within the prostate. On average, 76%±28% of tumor regions detected based on the proposed method were inside respective tumor areas identified in the histopathology images. Results of a linear regression analysis demonstrated a good correlation between the disease extents estimated using the reconstructed YM images and those determined from whole-mount histopathology images (r 2 =0.71). This pilot study demonstrates that the proposed method has a good potential for detection, localization and quantification of prostate cancer. The method can potentially be used for prostate needle biopsy guidance with the aim of decreasing the number of needle biopsies. The proposed technique utilizes conventional ultrasound imaging system only while no additional hardware attachment is required for mechanical stimulation or data acquisition. Therefore, the technique may be regarded as a non-invasive, low cost and potentially widely-available clinical tool for prostate cancer diagnosis., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

For studying cardiac mechanics, hyperelastic anisotropic computational models have been developed which require the tissue anisotropic and hyperelastic parameters. These parameters are obtained by tissue samples mechanically testing. The validity of such parameters are limited to the specific tissue sample only. They are not adaptable for pathological tissues commonly associated with tissue microstructure alterations. To investigate cardiac tissue mechanics, a novel approach is proposed to model hyperelasticity and anisotropy. This approach is adaptable to various tissue microstructural constituent's distributions in normal and pathological tissues. In this approach, the tissue is idealized as composite material consisting of cardiomyocytes distributed in extracellular matrix (ECM). The major myocardial tissue constituents are mitochondria and myofibrils while the main ECM's constituents are collagen fibers and fibroblasts. Accordingly, finite element simulations of uniaxial and equibiaxial tests of normal and infarcted tissue samples with known amounts of these constituents were conducted, leading to corresponding tissue stress-strain data that were fitted to anisotropic/hyperelastic models. The models were validated where they showed good agreement characterized by maximum average stress-strain errors of 16.17 and 10.01% for normal and infarcted cardiac tissue, respectively. This demonstrate the effectiveness of the proposed models in accurate characterization of healthy and pathological cardiac tissues.

In-depth understanding of the diaphragm's anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm's lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm's easy-to-segment contacting organs-including the lungs, heart, aorta, and ribcage-to guide the diaphragm's segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is [Formula: see text], which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology.

Developing suitable biomaterials for post-mastectomy or post-lumpectomy breast reconstruction is highly important. This study is aimed at evaluating biomechanical suitability of decellularized adipose tissue (DAT) for this purpose. The study involves computational experiments for evaluating deformation of the breast reconstructed using DAT under loading conditions pertaining to two common body position changes of prone-to-supine and prone-to-upright. This was conducted using nonlinear finite element models where the breast geometry was obtained from MRI image of a female breast. The experiments were performed using DAT sourced from various adipose tissue depots in comparison to natural adipose tissue. Data obtained from the conducted experiments showed no contour defects with various DAT materials for simulated post-mastectomy or post-lumpectomy breast reconstruction under the loading conditions. They also demonstrated that a breast reconstructed using DAT derived from the breast or subcutaneous abdominal depots exhibit significantly closer deformation, both qualitatively and quantitatively, to that of a normal breast under the same loading conditions. Similarity of DAT deformation to that of natural breast tissue in post-surgery breast reconstruction was assessed using nonlinear finite element analysis. Our results provide evidence that DAT derived from subcutaneous abdominal and breast depots yield more analogous deformation pattern to the natural tissue in post-mastectomy breast reconstruction applications. This is quite encouraging, as breast and subcutaneous adipose tissue can be readily obtained in large quantities from breast or abdominal lipo-reduction surgery procedures. Furthermore, in post-lumpectomy cases all DAT samples used in this research showed similar deformation, and thus are suitable as breast tissue substituents., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

To more closely mimic the native cellular microenvironment, 3D scaffolds derived from the extracellular matrix (ECM) are being developed as alternatives to conventional 2D culture systems. In the present study, we established methods to fabricate nonchemically cross-linked 3D porous foams derived entirely from decellularized porcine left ventricle (DLV) for use as an in vitro cardiac cell culture platform. Furthermore, we explored the effects of physically preprocessing the DLV through mechanical mincing versus cryomilling, as well as varying the ECM concentration on the structure, composition, and physical properties of the foams. Our results indicate that the less highly processed minced foams had a more cohesive and complex network of ECM components, enhanced mechanical properties, and improved stability under simulated culturing conditions. To validate the DLV foams, a proof-of-concept study was conducted to explore the early cardiomyogenic differentiation of pericardial fat adipose-derived stem/stromal cells (pfASCs) on the minced DLV foams relative to purified collagen I gel controls. Differentiation was induced using a modified cardiomyogenic medium (MCM) or through stimulation with 5-azacytidine (5-aza), and cardiomyocyte marker expression was characterized by immunohistochemistry and real-time reverse transcriptase-polymerase chain reaction. Our results indicate that early markers of cardiomyogenic differentiation were significantly enhanced on the DLV foams cultured in MCM, suggesting a synergistic effect of the cardiac ECM-derived scaffolds and the culture medium on the induction of pfASC differentiation. Furthermore, in analyzing the response in the noninduced control groups, the foams were observed to provide a mildly inductive microenvironment for pfASC cardiomyogenesis, supporting the rationale for using tissue-specific ECM as a substrate for cardiac cell culture applications.

Purpose: Association between tissue stiffness alteration and pathology is well known. This has formed the basis for prostate elastography imaging techniques where images of prostate tissue mechanical properties are reconstructed. In this paper, the authors present a novel prostate elastography technique which, unlike other techniques, relies on magnitude image data only., Methods: This proposed technique works in conjunction with ultrasound or magnetic resonance imaging (MRI) imaging modalities and it requires the prostate's pre- and postdeformation images as input. It uses a constrained reconstruction method where the elastic moduli of the prostate's normal and pathological tissues are determined based on an essential subset of the tissue deformation provided by the images data. The elasticity reconstruction technique uses optimization where similarity between calculated and observed shape features of the postcompression prostate image is maximized. The method was validated with an in silico phantom study followed by studies using ultrasound and MR with tissue-mimicking phantoms., Results: Using the proposed methods, it was observed that the maximum uncertainties of the reconstructed Young's modulus ratios of tumor to normal tissue were 15.6% and 9.7%, which were obtained from the transrectal ultrasound (TRUS) and MR tissue-mimicking phantom studies, respectively., Conclusions: This novel prostate elastography technique relies on prostate TRUS or MRI images that can be routinely acquired without additional imaging hardware. The phantom studies provided evidence that the proposed technique has a good potential to reconstruct prostate stiffness maps noninvasively particularly when applied in conjunction with MRI. Further studies are necessary to evaluate the technique's merits for clinical use.

Decellularized adipose tissue (DAT) has shown potential as a regenerative scaffold for plastic and reconstructive surgery to augment or replace damaged or missing adipose tissue (e.g. following lumpectomy or mastectomy). The mechanical properties of soft tissue substitutes are of paramount importance in restoring the natural shape and appearance of the affected tissues, and mechanical mismatching can lead to unpredictable scar tissue formation and poor implant integration. The goal of this work was to assess the linear elastic and hyperelastic properties of decellularized human adipose tissue and compare them to those of normal breast adipose tissue. To assess the influence of the adipose depot source on the mechanical properties of the resultant decellularized scaffolds, we performed indentation tests on DAT samples sourced from adipose tissue isolated from the breast, subcutaneous abdominal region, omentum, pericardial depot and thymic remnant, and their corresponding force-displacement data were acquired. Elastic and hyperelastic parameters were estimated using inverse finite element algorithms. Subsequently, a simulation was conducted in which the estimated hyperelastic parameters were tested in a real human breast model under gravity loading in order to assess the suitability of the scaffolds for implantation. Results of these tests showed that in the human breast, the DAT would show similar deformability to that of native normal tissue. Using the measured hyperelastic parameters, we were able to assess whether DAT derived from different depots exhibited different intrinsic nonlinearities. Results showed that DAT sourced from varying regions of the body exhibited little intrinsic nonlinearity, with no statistically significant differences between the groups., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Despite the success of elastography in grading hepatic fibrosis by stiffness related noninvasive markers the relationship between viscoelastic constants in the liver and tissue structure remains unclear. We therefore studied the mechanical properties of 16 human liver specimens with different degrees of fibrosis, inflammation and steatosis by wideband magnetic resonance elastography (MRE) and static indentation experiments providing the specimens׳ static Young׳s modulus (E), dynamic storage modulus (G') and dynamic loss modulus (G″). A frequency-independent shear modulus μ and a powerlaw exponent α were obtained by fitting G' and G″ using the two-parameter sprinpot model. The mechanical parameters were compared to the specimens׳ histology derived parameters such as degree of Fibrosis (F), inflammation score and fat score, amount of hydroxyproline (HYP) used for quantification of collagen, blood markers and presurgery in vivo function tests. The frequency averaged parameters G', G″ and μ were significantly correlated with F (G': R=0.762, G″: R=0.830; μ: R=0.744; all P<0.01) and HYP (G': R=0.712; G″: R=0.720; μ: R=0.731; all P<0.01). The powerlaw exponent α displayed an inverse correlation with F (R=-0.590, P=0.034) and a trend of inverse correlation with HYP (R=-0.470, P=0.089). The static Young׳s modulus E was less correlated with F (R=0.587, P=0.022) and not sensitive to HYP. Although inflammation was highly correlated with F (R=0.773, P<0.001), no interaction was discernable between inflammation and mechanical parameters measured in this study. Other histological and blood markers as well as liver function test were correlated with neither F nor the measured mechanical parameters. In conclusion, viscoelastic constants measured by wideband MRE are highly sensitive to histologically proven fibrosis. Our results suggest that, in addition to the amount of connective tissue, subtle structural changes of the viscoelastic matrix determine the sensitivity of mechanical tissue properties to hepatic fibrosis., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Compressibility of biological tissues such as brain parenchyma is related to its poroelastic nature characterized by the geometry and pressure of vasculature and interconnected fluid-filled spaces. Thus, cerebral volumetric strain may be sensitive to intracranial pressure which can be altered under physiological conditions. So far volumetric strain has attained little attention in studies of the mechanical behavior of the brain. This paper reports a study of measuring the in vivo cerebral volumetric strain induced by the Valsalva maneuver (VM) where forced expiration against a closed glottis leads to a transient increase in the intracranial pressure. For this purpose we applied three-dimensional magnetic resonance imaging equipped with a patient-controlled acquisition system to five healthy volunteers. With each volunteer, three experiments were performed: one with VM and two in resting state. i.e. normal ventilation, which were conducted before and after VM. The VM data were registered to reference data by morphology based non-rigid deformation, yielding 3D maps of total displacements and volumetric strain. On average, VM induced volumetric strain correlated to whole-brain dilatation of -3.14±0.87% and -2.80±0.71% compared to the reference states before and after VM, respectively. These values were well reproduced by repetitive experiments during the same scan as well as by repeated measurements in one volunteer on different days. Combined with literature data of intracranial pressure changes, our volumetric strain values can be used to elucidate the static compression modulus of the in vivo human brain. These results add knowledge to the understanding of the brain׳s biomechanical properties under physiological conditions., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Purpose: Various types of cancers including prostate cancer are known to be associated with biological changes that lead to tissue stiffening. Digital rectal examination is based on manually palpating the prostate tissue via the rectum. This test lacks sufficient accuracy required for early diagnosis which is necessary for effective management of prostate cancer. To develop an effective prostate cancer diagnostic technique, the authors propose an imaging technique that maps the distribution of the relative prostate tissue's elasticity modulus. Unlike digital rectal examination, this technique is quantitative, capable of accurately detecting small prostate lesions that cannot be sensed by manual palpation, and its accuracy is independent of the physician's experience., Methods: The proposed technique is a quasistatic elastography technique which uses ultrasound imaging to acquire tissue displacements resulting from transrectal ultrasound mechanical stimulation. The system involves a standard ultrasound imaging unit with accessibility to its radiofrequency data. The displacements are used as data for the tissue elasticity reconstruction. This reconstruction does not require tissue segmentation and is based on physics governing tissue mechanics. It is formulated using an inverse problem framework where elastic tissue deformation equations are fully inverted using an iterative scheme where each iteration involves stress calculation followed by elastic modulus updating until convergence is achieved.In silico and tissue mimicking phantom studies were conducted to validate the proposed technique, followed by a clinical pilot study involving two prostate cancer patients with whole-mount histopathology analysis on prostatectomy specimens to confirm a cancer location., Results: The phantom studies demonstrated robustness and reasonably high accuracy of the proposed method. Obtained Young's modulus ratios indicated reconstruction errors of less than 12%. Reconstructed elastic modulus images of the two clinical cases were compared to whole-mount histopathology slides where cancerous areas were identified. This comparison indicated marked tissue stiffening in the cancer area with reasonably accurate consistency observed between cancerous lesions identified by histopathology and high stiffness areas of the elastography images., Conclusions: Results obtained from the phantom and patient studies indicate that the proposed method is reasonably accurate for detecting cancerous lesions. The proposed system does not require any additional hardware attachment for mechanical stimulation or data acquisition while the elasticity reconstruction algorithm can be easily implemented, leading to a low cost system that can be potentially utilized as an effective clinical tool for prostate cancer diagnosis.

Purpose: Accurate Transrectal Ultrasound (TRUS)-guided prostate needle biopsy requires registering preoperative 3D TRUS or MR image, in which tumors and other suspicious areas are visible, to intraoperative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, robotic needle biopsy systems can be used to place the US probe at the same position relative to the prostate during the 3D and 2D image acquisition to ensure similar prostate deformation. To have such similar deformation, only visual feedback is not sufficient as such feedback can be used to only guarantee that the whole prostate is within the field of view irrespective of the probe's orientation. As such, contact pressure feedback can be utilized to ensure consistent minimum contact between the probe and prostate., Method: A robotic system is proposed where a TRUS probe with pressure sensor array is used. The contact pressure can be measured during imaging and used to provide feedback in conjunction with an optimization algorithm for consistent probe positioning. The robotic system is driven by the feedback to position the probe such that pressure pattern of the sensors during 2D image acquisition is similar to the pressure pattern during 3D image acquisition. The proposed method takes into account the patient's body movement expected during image acquisition. In this study, an in silico phantom is used where the simulated contact pressure distribution required in the optimization algorithm is obtained using a prostate finite element model., Result: Starting from an arbitrary position where the probe contacts the phantom, this position was varied systematically until a position corresponding to maximum pressure pattern similarity between contact pressure patterns corresponding to the 2D and 3D imaging was achieved successfully., Conclusion: Results obtained from the in silico phantom study indicate that the proposed technique is capable of ensuring having only minimal relative prostate deformation between preoperative image acquisition and intraoperative imaging used for guiding needle biopsy, paving the way for faster and more accurate registration.

The quasi-static Young's modulus of the eardrum's pars tensa is an important modeling parameter in computer simulations. Recent developments in indentation testing and inverse modeling allow estimation of this parameter with the eardrum in situ. These approaches are challenging because of the curved shape of the pars tensa which requires special care during experimentation to keep the indenter perpendicular to the local surface at the point of contact. Moreover, they involve complicated contact modeling. An alternative computer-based method is presented here in which pressurization is used instead of indentation. The Young's modulus of a thin-shell model of the eardrum with subject-specific geometry is numerically optimized such that simulated pressurized shapes match measured counterparts. The technique was evaluated on six healthy rat eardrums, resulting in a Young's modulus estimate of 22.8±1.5MPa. This is comparable to values estimated using indentation testing. The new pressurization-based approach is simpler to use than the indentation-based method for the two reasons noted above., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Purpose: A novel technique is proposed to characterize lung tissue incompressibility variation during respiration. Estimating lung tissue incompressibility parameter variations resulting from air content variation throughout respiration is critical for computer assisted tumor motion tracking. Continuous tumor motion is a major challenge in lung cancer radiotherapy, especially with external beam radiotherapy. If not accounted for, this motion may lead to areas of radiation overdosage for normal tissue. Given the unavailability of imaging modality that can be used effectively for real-time lung tumor tracking, computer assisted approach based on tissue deformation estimation can be a good alternative. This approach involves lung biomechanical model where its fidelity depends on input tissue properties. This investigation shows that considering variable tissue incompressibility parameter is very important for predicting tumor motion accurately, hence improving the lung radiotherapy outcome., Methods: First, an in silico lung phantom study was conducted to demonstrate the importance of employing variable Poisson's ratio for tumor motion predication. After it was established that modeling this variability is critical for accurate tumor motion prediction, an optimization based technique was developed to estimate lung tissue Poisson's ratio as a function of respiration cycle time. In this technique, the Poisson's ratio and lung pressure value were varied systematically until optimal values were obtained, leading to maximum similarity between acquired and simulated 4D CT lung images. This technique was applied in an ex vivo porcine lung study where simulated images were constructed using the end exhale CT image and deformation fields obtained from the lung's FE modeling of each respiration time increment. To model the tissue, linear elastic and Marlow hyperelastic material models in conjunction with variable Poisson's ratio were used., Results: The phantom study showed that the tumor motion trajectory and its final locations obtained from simulations with and without considering tissue incompressibility variation were very different. For example, tumor displacements in the z direction were -11.23 and -38.10 mm obtained with the Marlow hyperelastic material model in conjunction with constant and variable Poisson's ratio, respectively. By comparing the acquired 4D-CT image sequence of the porcine lung with their image sequence counterparts obtained from the hyperelastic model with constant and variable Poisson's ratio, it was shown that using variable tissue incompressibility reduced errors significantly in tumor motion prediction., Conclusions: This investigation demonstrates the importance of incompressibility variation estimation and utilization for accurate tumor tracking in computer assisted lung external beam radiation therapy. An optimization framework was developed to estimate a Poisson's ratio function in terms of respiration cycle time using experimental image data of the lung. Utilizing this function along with respiratory system FE modeling may lead to more effective tumor targeting, hence potentially improving the outcome of lung external beam radiation therapy techniques. This is particularly true for stereotactic body radiation therapy where only one or a few fraction treatments are applied, precluding the possibility of averaging out dosimetric deviations introduced by the respiratory motion.

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.

To design tissue-specific bioscaffolds with well-defined properties and 3-D architecture, methods were developed for preparing porous foams from enzyme-solubilized human decellularized adipose tissue (DAT). Additionally, a technique was established for fabricating "bead foams" comprised of interconnected networks of porous DAT beads fused through a controlled freeze-thawing and lyophilization procedure. In characterization studies, the foams were stable without the need for chemical crosslinking, with properties that could be tuned by controlling the protein concentration and freezing rate during synthesis. Adipogenic differentiation studies with human adipose-derived stem cells (ASCs) suggested that stiffness influenced ASC adipogenesis on the foams. In support of our previous work with DAT scaffolds and microcarriers, the DAT foams and bead foams strongly supported adipogenesis and were also adipo-inductive, as demonstrated by glycerol-3-phosphate dehydrogenase (GPDH) enzyme activity, endpoint RT-PCR analysis of adipogenic gene expression, and intracellular lipid accumulation. Adipogenic differentiation was enhanced on the microporous DAT foams, potentially due to increased cell-cell interactions in this group. In vivo assessment in a subcutaneous Wistar rat model demonstrated that the DAT bioscaffolds were well tolerated and integrated into the host tissues, supporting angiogenesis and adipogenesis. The DAT-based foams induced a strong angiogenic response, promoted inflammatory cell migration and gradually resorbed over the course of 12 weeks, demonstrating potential as scaffolds for wound healing and soft tissue regeneration., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A computational model is proposed to demonstrate the feasibility of characterizing the motion of lung tumors caused by respiratory diaphragm forces using a tissue biomechanics approach. Compensating for such motion is very important for developing effective systems of minimally invasive tumor ablative procedures, e.g., Low Dose Rate (LDR) lung brachytherapy. To minimize the effects of respiratory motion, the target lung is almost completely deflated before starting such procedures. However, a significant amount of motion persists in the target lung due to the diaphragm contact forces required for the other lung's respiration. In this study, a model pipeline was developed which inputs a pre-operative 4D-CT image sequence of the lung to output the predicted 3D motion trajectory of the tumor over the respiratory cycle. A finite element method was used in this pipeline to model the lung tissue deformation in order to predict the tumor motion. Experiments were conducted on an ex vivo porcine lung to demonstrate the performance and assess the accuracy of the proposed pipeline. The resultant tumor motion trajectory obtained from the biomechanical model of the lung was compared to the experimental trajectory obtained from CT imaging. Results were promising, suggesting that tissue mechanics-based modeling can be employed for effective characterization of lung tumor respiratory motion to improve accuracy in lung tumor ablative procedures.

The finite element (FE) method can accurately calculate tissue deformation. However, its low speed renders it ineffective for many biomedical applications involving real-time data processing. To accelerate FE analysis, we introduce a novel tissue mechanics simulation technique. This technique is suitable for real-time estimation of tissue deformation of specific organs, which is required in computer-aided diagnostic or therapeutic procedures. In this method, principal component analysis is used to describe each organ shape and its corresponding FE field for a pool of patients by a small number of weight factors. A mapping function is developed to relate the parameters of organ shape to their FE field counterpart. We show that irrespective of the complexity of the tissue's constitutive law or its loading conditions, the proposed technique is highly accurate and fast in estimating the FE field. Average deformation errors of less than 2% demonstrate the accuracy of the proposed technique.

Hyperelastic properties of deflated lung tissue have been characterized via an inverse finite element approach. Such properties are useful in many medical diagnosis and treatment applications where tissue deformation can be modeled to account for during the procedure. Several indentation experiments were conducted on various porcine lungs' tissue specimens resected immediately from different regions and lobes after the animals were sacrificed. Three different strain energy models, namely Ogden, Yeoh, and Polynomial, were used and respective hyperelastic parameters were obtained. The parameters for each model were estimated through an optimization process where the experimental force-displacement profiles of indentation were fitted to those obtained from finite element simulations performed specifically for the samples' geometries. Results obtained in this investigation for all the three models indicate convergence with reasonably low average fitting errors ranging from 2.3% to 6.2%. Independent tests were also performed to assess the effects of samples' heterogeneities on the obtained parameters. The outcome of these tests was encouraging and confirmed small impact of tissue inhomogeneities on the estimated parameters. The reported hyperelastic properties can, accordingly, pave the way for more accurate biomechanical modeling of the lung's soft tissue in the emerging applications of minimally invasive medical intervention for lung cancer diagnosis and treatment.

Purpose: A novel technique is proposed to construct CT image of a totally deflated lung from a free-breathing 4D-CT image sequence acquired preoperatively. Such a constructed CT image is very useful in performing tumor ablative procedures such as lung brachytherapy. Tumor ablative procedures are frequently performed while the lung is totally deflated. Deflating the lung during such procedures renders preoperative images ineffective for targeting the tumor. Furthermore, the problem cannot be solved using intraoperative ultrasound (U.S.) images because U.S. images are very sensitive to small residual amount of air remaining in the deflated lung. One possible solution to address these issues is to register high quality preoperative CT images of the deflated lung with their corresponding low quality intraoperative U.S. images. However, given that such preoperative images correspond to an inflated lung, such CT images need to be processed to construct CT images pertaining to the lung's deflated state., Methods: To obtain the CT images of deflated lung, we present a novel image construction technique using extrapolated deformable registration to predict the deformation the lung undergoes during full deflation. The proposed construction technique involves estimating the lung's air volume in each preoperative image automatically in order to track the respiration phase of each 4D-CT image throughout a respiratory cycle; i.e., the technique does not need any external marker to form a respiratory signal in the process of curve fitting and extrapolation. The extrapolated deformation field is then applied on a preoperative reference image in order to construct the totally deflated lung's CT image. The technique was evaluated experimentally using ex vivo porcine lung., Results: The ex vivo lung experiments led to very encouraging results. In comparison with the CT image of the deflated lung we acquired for the purpose of validation, the constructed CT image was very similar. The intensity mean absolute difference between these two images was calculated to be at 1%. Tumor center as well as a number of anatomical fiducial markers were traced in different corresponding slices of the two images. The average misalignment obtained for the constructed CT image was (0.64, 0.39, 0.11) mm, which indicates a very desirable accuracy for lung brachytherapy applications., Conclusions: The image construction accuracy obtained in this research is suitable for intraoperative tasks; e.g., tumor localization and fusing with real time navigation data in lung brachytherapy. These applications involve image registration with intraoperative U.S. images in order to enhance their poor quality. The proposed technique is also useful for preoperative tasks such as planning of lung brachytherapy treatment.

A respiratory image-sequence-segmentation technique is introduced based on a novel image-sequence analysis. The proposed technique is capable of segmenting the lung's air and its soft tissues followed by estimating the lung's air volume and its variations throughout the image sequence. Accurate estimation of these two parameters is very important in many applications related to lung disease diagnosis and treatment systems (e.g., brachytherapy), where the parameters are either the variables of interest themselves or are dependent/independent variables. The concept of the proposed technique involves using the image sequence's combined histogram to obtain a reasonable initial guess for the lung's air segmentation thresholds. This is followed by an optimization process to find the optimum threshold values that best satisfy the lung's air mass conservation and tissue incompressibility principles. These threshold values are consequently applied to estimate the lung's air volume and its variations throughout respiratory Computed Tomography (CT) image sequences. Ex vivo experiments were conducted on porcine left lungs in order to demonstrate the performance of the proposed technique. The proposed method was initially validated using a breath-hold CT image sequence with known air volumes inside the lung, where results show that the proposed technique outperforms single-histogram-based methods. This was followed by demonstrating the proposed technique's application in a 4-D-CT respiratory sequence, where the air volume inside the lung was unknown. Consistency of the obtained results in the latter experiment with tissue near incompressibility principle was validated. The results indicate a very good ability of the proposed method for estimating the lung's air volume and its variations in a respiratory image sequence.

In breast elastography, breast tissue usually undergoes large compression resulting in significant geometric and structural changes. This implies that breast elastography is associated with tissue nonlinear behavior. In this study, an elastography technique is presented and an inverse problem formulation is proposed to reconstruct parameters characterizing tissue hyperelasticity. Such parameters can potentially be used for tumor classification. This technique can also have other important clinical applications such as measuring normal tissue hyperelastic parameters in vivo. Such parameters are essential in planning and conducting computer-aided interventional procedures. The proposed parameter reconstruction technique uses a constrained iterative inversion; it can be viewed as an inverse problem. To solve this problem, we used a nonlinear finite element model corresponding to its forward problem. In this research, we applied Veronda-Westmann, Yeoh and polynomial models to model tissue hyperelasticity. To validate the proposed technique, we conducted studies involving numerical and tissue-mimicking phantoms. The numerical phantom consisted of a hemisphere connected to a cylinder, while we constructed the tissue-mimicking phantom from polyvinyl alcohol with freeze-thaw cycles that exhibits nonlinear mechanical behavior. Both phantoms consisted of three types of soft tissues which mimic adipose, fibroglandular tissue and a tumor. The results of the simulations and experiments show feasibility of accurate reconstruction of tumor tissue hyperelastic parameters using the proposed method. In the numerical phantom, all hyperelastic parameters corresponding to the three models were reconstructed with less than 2% error. With the tissue-mimicking phantom, we were able to reconstruct the ratio of the hyperelastic parameters reasonably accurately. Compared to the uniaxial test results, the average error of the ratios of the parameters reconstructed for inclusion to the middle and external layers were 13% and 9.6%, respectively. Given that the parameter ratios of the abnormal tissues to the normal ones range from three times to more than ten times, this accuracy is sufficient for tumor classification.

A technique is proposed to enhance the quality of intraoperative ultrasound (US) images of a deflated lung undergoing minimally invasive tumor ablative procedure. Since US images are very sensitive to residual air remaining in deflated lung, lung US images have very poor quality, and hence, are not appropriate for image-guided procedures. Therefore, a reliable and high-quality intraoperative image of the lung is a paramount necessity for tumor localization and fusion with real-time navigation data during such procedures. The proposed technique employs information of a deflated lung's computed tomography (CT) image constructed preoperatively in order to enhance those of the intraoperative US images. The enhancement is performed via two concurrent registration processes. The output is an enhanced US image of the deflated lung oriented and positioned accurately within its preoperative CT counterpart. Ex vivo experiments were conducted to evaluate the performance of the proposed technique. The obtained results indicate that very considerable improvement was achieved in the quality of the input intraoperative US images of the deflated lung.

Accurate estimation of the quasi-static Young's modulus of the eardrum is important for finite-element modeling. In this study, we adapted a tissue indentation technique and inverse finite-element analysis to estimate the Young's modulus of the eardrum. A custom-built indentation apparatus was used to perform indentation testing on seven rat eardrums in situ after immobilizing the malleus. Testing was done in most cases on the posterior pars tensa. The unloaded shape of each eardrum was measured and used to construct finite-element models with subject-specific geometries to simulate the indentation experiment. The Young's modulus of each specimen was then estimated by numerically optimizing the Young's modulus of each model so that simulation results matched corresponding experimental data. Using an estimated value of 12 microm for the thickness of each model eardrum, the estimated average Young's modulus for the pars tensa was found to be 21.7+/-1.2 MPa. The estimated average Young's modulus is within the range reported in some of the literature. The estimation technique is sensitive to the thickness of the pars tensa used in the model but is not sensitive to relatively large variations in the stiffness of the pars flaccida and manubrium or to the pars tensa/pars flaccida separation conditions., (Copyright (c) 2010 Elsevier B.V. All rights reserved.)

Purpose of Study: To compare magnetic resonance elastography (MRE) with ventricular pressure changes in an animal model., Methods: Three pigs of different cardiac physiology (weight, 25 to 53 kg; heart rate, 61 to 93 bpm; left ventricular [LV] end-diastolic volume, 35 to 70 ml) were subjected to invasive LV pressure measurement by catheter and noninvasive cardiac MRE. Cardiac MRE was performed in a short-axis view of the heart and applying a 48.3-Hz shear-wave stimulus. Relative changes in LV-shear wave amplitudes during the cardiac cycle were analyzed. Correlation coefficients between wave amplitudes and LV pressure as well as between wave amplitudes and LV diameter were determined., Results: A relationship between MRE and LV pressure was observed in all three animals (R2 >or= 0.76). No correlation was observed between MRE and LV diameter (R2

The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community. There are several biomedical applications where parameters characterizing such properties are critical for a reliable clinical outcome. These applications include surgery planning, needle biopsy and brachtherapy where tissue biomechanical modeling is involved. Another important application is interpreting nonlinear elastography images. While there has been considerable research on the measurement of the linear elastic modulus of small tissue samples, little research has been conducted for measuring parameters that characterize the nonlinear elasticity of tissues included in tissue slice specimens. This work presents hyperelastic measurement results of 44 pathological ex vivo breast tissue samples. For each sample, five hyperelastic models have been used, including the Yeoh, N = 2 polynomial, N = 1 Ogden, Arruda-Boyce, and Veronda-Westmann models. Results show that the Yeoh, polynomial and Ogden models are the most accurate in terms of fitting experimental data. The results indicate that almost all of the parameters corresponding to the pathological tissues are between two times to over two orders of magnitude larger than those of normal tissues, with C(11) showing the most significant difference. Furthermore, statistical analysis indicates that C(02) of the Yeoh model, and C(11) and C(20) of the polynomial model have very good potential for cancer classification as they show statistically significant differences for various cancer types, especially for invasive lobular carcinoma. In addition to the potential for use in cancer classification, the presented data are very important for applications such as surgery planning and virtual reality based clinician training systems where accurate nonlinear tissue response modeling is required.

Many cardiovascular diseases and disorders are associated with hemodynamic dysfunction. The heart's ability to contract and pump blood through the vascular system primarily depends on the elasticity of the myocardium. This article introduces a magnetic resonance elastography (MRE) technique that allows noninvasive and time-resolved measurement of changes in myocardial elasticity over the cardiac cycle. To this end, low-frequency shear vibrations of 24.3 Hz were induced in the human heart via the anterior chest wall. An electrocardiograph (ECG)-triggered, steady-state MRE sequence was used to capture shear oscillations with a frame rate of eight images per vibration cycle. The time evolution of 2D-shear wave fields was observed in two imaging planes through the short axis of the heart in six healthy volunteers. Correlation analysis revealed that wave amplitudes were modulated in synchrony to the heartbeat with up to 2.45 +/- 0.18 higher amplitudes during diastole than during systole (interindividual mean +/- SD). The reduction of wave amplitudes started at 75 +/- 9 ms prior to changes in left ventricular diameter occurring at the beginning of systole. Analysis of this wave amplitude alteration using a linear elastic constitutive model revealed a maximum change in the myocardial wall stiffness of a factor of 37.7 +/- 10.6 during the cardiac cycle.

Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Noninvasive imaging tools that better characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors which lead to the development of unstable lesions, and identify patients at risk of plaque disruption and stroke. Carotid magnetic resonance (MR) imaging allows for the characterization of carotid vessel wall and plaque composition, the characterization of normal and pathological arterial wall, the quantification of plaque size, and the detection of plaque integrity. On the other hand, various ultrasound (US) measurements have also been used to quantify atherosclerosis, carotid stenosis, intima-media thickness, total plaque volume, total plaque area, and vessel wall volume. Combining the complementary information provided by 3D MR and US carotid images may lead to a better understanding of the underlying compositional and textural factors that define plaque and wall vulnerability, which may lead to better and more effective stroke prevention strategies and patient management. Combining these images requires nonrigid registration to correct the nonlinear misalignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisition sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, a "twisting and bending" model was used with only six parameters to model the normal movement of the neck for nonrigid registration. The registration technique was evaluated using 3D US and MR carotid images at two field strengths, 1.5 and 3.0 T, of the same subject acquired on the same day. The mean registration error between the segmented carotid artery wall boundaries in the target US image and the registered MR images was calculated using a distance-based error metric after applying a "twisting and bending" model based nonrigid registration algorithm. An average registration error of 1.4 +/- 0.3 mm was obtained for 1.5 T MR and 1.5 +/- 0.4 mm for 3.0 T MR, when registered with 3D US images using the nonrigid registration technique presented in this paper. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with this nonrigid registration technique compared to rigid registration.