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Purpose: Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasistatic 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 Signalto-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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a technique aimed at accurate measurement of electrical permittivity (EP) of small tissue specimens at low frequencies. Accurate measurement of EP of healthy and pathological tissues can facilitate the establishment of many medical applications such as electrical impedance imaging. The proposed novel technique employs hardware with high precision for impedance measurement of capacitive structures formed by the tissue specimen as its dielectric combined with two conductive plates placed at two ends of the specimen. To determine the EP of the tissue specimen, the capacitance part of the measured impedance is processed using an inverse finite-element framework. In this framework, accurate geometry and boundary conditions of the specimen are considered. The proposed technique was validated before it was employed to measure EP of several tissue specimens including bovine heart, liver, and bone. Results obtained in this investigation indicate that the proposed technique is reliable as it provides improved EP measurement accuracy and repeatability of homogeneous or multilayered tissue specimens, especially at low-frequency range. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous studies have shown that dielectric properties of biological tissues can be imaged at high frequencies (50 MHz–20 GHz) to detect abnormalities such as tumors. While evidence suggests that imaging these properties at low frequencies (e.g. below 1 MHz) holds a good potential in medical applications, less research efforts have been dedicated to explore these properties at such frequencies for medical imaging. This study uses a recently developed technique to measure tissue dielectric properties of normal and corresponding cancerous tissue at low frequencies. This was accomplished by using a preclinical animal tumor model. To develop this animal model, human breast cancer cell line (MDA-MB-231) was injected into hind legs of severely compromised immunodeficient (SCID) mice. As a result, tumors were developed while they were permitted to grow to the size of 8–10 mm in 8 weeks. The electrical conductivity and permittivity (EC and EP) of the grown xenograft tumors and their surrounding normal tissue were measured at 100 Hz–1 MHz frequency using a measurement method which includes using a custom-made experimental setup in conjunction with an inverse finite element framework. Histological analysis was performed on the tumor and normal tissue specimens to assess differences in their micro-structure. Results indicated that both conductivity and permittivity of the tumors have significantly greater values than those of the surrounding normal tissue with average ratio values of 3.5:1 and 10.9:1 for the EC and EP, respectively. Results obtained in this study are consistent with micro-structural changes observed by histological assessment. The substantially high EP ratios measured in this study suggests that electrical permittivity at low frequencies can potentially be used as a powerful biomarker for the detection of breast malignancies. [ABSTRACT FROM AUTHOR]