Abbas Samani

Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples.

Understanding and quantifying the mechanical properties of breast tissues has been a subject of interest for the past two decades. This has been motivated in part by interest in modelling soft tissue response for surgery planning and virtual-reality-based surgical training. Interpreting elastography images for diagnostic purposes also requires a sound understanding of normal and pathological tissue mechanical properties. Reliable data on tissue elastic properties are very limited and those which are available tend to be inconsistent, in part as a result of measurement methodology. We have developed specialized techniques to measure tissue elasticity of breast normal tissues and tumour specimens and applied them to 169 fresh ex vivo breast tissue samples including fat and fibroglandular tissue as well as a range of benign and malignant breast tumour types. Results show that, under small deformation conditions, the elastic modulus of normal breast fat and fibroglandular tissues are similar while fibroadenomas were approximately twice the stiffness. Fibrocystic disease and malignant tumours exhibited a 3-6-fold increased stiffness with high-grade invasive ductal carcinoma exhibiting up to a 13-fold increase in stiffness compared to fibrogalndular tissue. A statistical analysis showed that differences between the elastic modulus of the majority of those tissues were statistically significant. Implications for the specificity advantages of elastography are reviewed.

A method to measure the hyperelastic parameters of ex vivo breast tissue samples

Over the past decade, there has been increasing interest in modelling soft tissue deformation. This topic has several biomedical applications ranging from medical imaging to robotic assisted telesurgery. In these applications, tissue deformation can be very large due to low tissue stiffness and lack of physical constraints. As a result, deformation modelling of such organs often requires a treatment, which reflects nonlinear behaviour. While computational techniques such as nonlinear finite element methods are well developed, the required intrinsic nonlinear mechanical parameters of soft tissues that are critical to develop reliable tissue deformation models are not well known. To address this issue, we developed a system to measure the hyperelastic parameters of small ex vivo tissue samples. This measurement technique consists of indenting an unconfined small block of tissue using a computer controlled loading system while measuring the resulting indentation force. The nonlinear tissue force-displacement response is used to calculate the hyperelastic parameters via an appropriate inversion technique. This technique is based on a nonlinear least squares formulation that uses a nonlinear finite element model as the direct problem solver. The features of the system are demonstrated with two samples of breast tissue and typical hyperelastic results are presented.

Measurement of the hyperelastic properties of ex vivo brain tissue slices

The elastic and hyperelastic properties of brain tissue are of interest to the medical research community as there are several applications where accurate characterization of these properties is crucial for an accurate outcome. The linear response is applicable to brain elastography, while the non-linear response is of interest for surgical simulation programs. Because of the biological differences between gray and white matter, it is reasonable to expect a difference in their mechanical properties. The goal of this work is to characterize the elastic and hyperelastic properties of the brain gray and white matter. In this method, force-displacement data of these tissues were acquired from 25 different brain samples using an indentation apparatus. These data were processed with an inverse problem algorithm using finite element method as the forward problem solver. Youngs modulus and the hyperelastic parameters corresponding to the commonly used Polynomial, Yeoh, Arruda-Boyce, and Ogden models were obtained. The parameters characterizing the linear and non-linear mechanical behavior of gray and white matters were found to be significantly different. Youngs modulus was 1787±186 and 1195±157Pa for white matter and gray matter, respectively. Among hyperelastic models, due to its accuracy, fewer parameters and shorter computational time requirements, Yeoh model was found to be the most suitable. Due to the significant differences between the linear and non-linear tissue response, we conclude that incorporating these differences into brain biomechanical models is necessary to increase accuracy.

MR elastography of the human heart: Noninvasive assessment of myocardial elasticity changes by shear wave amplitude variations

Many cardiovascular diseases and disorders are associated with hemodynamic dysfunction. The hearts 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. Magn Reson Med, 2009.

An inverse problem solution for measuring the elastic modulus of intact ex vivo breast tissue tumours

Soft tissue elasticity has been a subject of interest in biomedical applications as an aid to medical diagnosis since the dawn of medicine. More recently, this has led to the concept of elastography with the aim of imaging the spatial distribution of tissue elasticity. Interpreting elastography images requires reliable information pertaining to elastic properties of normal and pathological tissues. Such information is either very limited or not available in the literature. Elastic modulus measurement techniques developed for soft tissues generally require tissue excision to prepare samples for testing. While this may be done with normal tissues, tumour tissue excision is generally not permissible because tumour pathological assessment requires that the tumour be kept intact. To address this limitation, we developed a system to measure the Youngs modulus of tumour specimens. The technique consists of indenting the tumour specimen while measuring indentation force and displacements. To obtain the Youngs modulus from the measured force-displacement slope, we developed an iterative inversion technique that uses a finite element model of the piecewise homogeneous tissue slice in each iteration. Preliminary elasticity measurement results of various breast tumours are presented and discussed. These results indicate that the proposed method is robust and highly accurate. Furthermore, they indicate that a benign lesion and malignant tumours are roughly five times and ten times stiffer than normal breast tissues respectively.

Measurement of the hyperelastic properties of 44 pathological ex vivo breast tissue samples.

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.

Cardiac MR Elastography: Comparison with left ventricular pressure measurement

Purpose of studyTo compare magnetic resonance elastography (MRE) with ventricular pressure changes in an animal model.MethodsThree 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.ResultsA relationship between MRE and LV pressure was observed in all three animals (R2 ≥ 0.76). No correlation was observed between MRE and LV diameter (R2 ≤ 0.15). Instead, shear wave amplitudes decreased 102 ± 58 ms earlier than LV diameters at systole and amplitudes increased 175 ± 40 ms before LV dilatation at diastole. Amplitude ratios between diastole and systole ranged from 2.0 to 2.8, corresponding to LV pressure differences of 60 to 73 mmHg.ConclusionExternally induced shear waves provide information reflecting intraventricular pressure changes which, if substantiated in further experiments, has potential to make cardiac MRE a unique noninvasive imaging modality for measuring pressure-volume function of the heart.

Measurement of the hyperelastic properties of tissue slices with tumour inclusion.

The elastic and hyperelastic properties of biological soft tissues have been of interest to the medical community as there are several applications where parameters characterizing these properties are critical for a reliable outcome. This includes applications such as surgery planning, needle biopsy and cancer diagnosis using medical imaging. 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 nonlinear elasticity of tissues included in slice specimens. In this paper, we present a method of measuring the hyperelastic parameters of tissue slice samples with tumours. In this method, to measure the hyperelastic properties of a tumour within a slice sample, the tumour was indented to acquire its force-displacement response while the slice remained intact. To calculate the hyperelastic parameters from the acquired data, we developed two inversion techniques that use the slice nonlinear finite element model as their forward problem solver. One of these techniques was based on nonlinear optimization while the other is a novel iterative technique that processes the variable slopes of the force-displacement response to calculate the hyperelastic parameters. The latter was developed specifically for the Yeoh and the second-order polynomial hyperelastic models, since we found that the other optimization-based inversion technique did not perform well with these models. To validate the proposed techniques, we performed numerical and phantom experiments. While we were able to achieve convergence with wide ranges of parameters of initial guesses to within 1% error with the numerical simulation experiments, we achieved convergence to within errors of around 5% with the tissue mimicking phantoms. Moreover, we successfully applied these techniques to data we acquired from nine pathological breast tissue slice specimens where the goal was to determine the hyperelastic properties of the tumour within the breast tissue slices.

A constrained reconstruction technique of hyperelasticity parameters for breast cancer assessment.

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.

Statistical finite element method for real-time tissue mechanics analysis

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 tissues 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.