Characterization and Assessment of Mechanical Properties of Adipose Derived Breast Tissue Scaffolds as a Means for Breast Reconstructive Purposes

Decellularized adipose tissue (DAT) has shown great potential for use as a regenerative scaffold in breast reconstruction following mastectomies or lumpectomies. Mechanical properties of such scaffolds are of great importance in order to mimic natural adipose tissue. This study focuses on the characterization of mechanical properties and assessment of DAT scaffolds for implantation into a human breast. DAT samples sourced from multiple adipose tissue depots within the body were tested and their elastic and hyperelastic parameters were obtained. Subsequently simulations were conducted where the calculated hyperelastic parameters were tested as a real human breast model under two different gravity loading situations (prone-to-supine, and prone-to-upright positions). DAT samples were also modelled for post-mastectomy, and post-lumpectomy reconstruction purposes. Results show that DAT shows similar deformability to that of native tissue, and varying DAT depots exhibited little intrinsic nonlinearity. Finally, contour defects were not observed for the samples under either loading conditions

Elastography Method for Reconstruction of Nonlinear Breast Tissue Properties

Elastography is developed as a quantitative approach to imaging linear elastic properties of tissues to detect suspicious tumors. In this paper a nonlinear elastography method is introduced for reconstruction of complex breast tissue properties. The elastic parameters are estimated by optimally minimizing the difference between the computed forces and experimental measures. A nonlinear adjoint method is derived to calculate the gradient of the objective function, which significantly enhances the numerical efficiency and stability. Simulations are conducted on a three-dimensional heterogeneous breast phantom extracting from real imaging including fatty tissue, glandular tissue, and tumors. An exponential-form of nonlinear material model is applied. The effect of noise is taken into account. Results demonstrate that the proposed nonlinear method opens the door toward nonlinear elastography and provides guidelines for future development and clinical application in breast cancer study

Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step

Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues

[EN] This paper presents a novel methodology to in-vivo estimate the elastic constants of a constitutive model proposed to characterize the mechanical behavior of the breast tissues. An iterative search algorithm based on genetic heuristics was constructed to in-vivo estimate these parameters using only medical images, thus avoiding invasive measurements of the mechanical response of the breast tissues. For the first time, a combination of overlap and distance coefficients were used for the evaluation of the similar- ity between a deformed MRI of the breast and a simulation of that deformation. The methodology was validated using breast software phantoms for virtual clinical trials, compressed to mimic MRI-guided biopsies. The biomechanical model chosen to characterize the breast tissues was an anisotropic neo-Hookean hyperelastic model. Results from this analysis showed that the algorithm is able to find the elastic constants of the constitutive equations of the proposed model with a mean relative error of about 10%. Furthermore, the overlap between the reference deformation and the simulated deformation was of around 95% showing the good performance of the proposed methodology. This methodology can be easily extended to characterize the real biomechanical behavior of the breast tissues, which means a great novelty in the field of the simulation of the breast behavior for applications such as surgical planing, surgical guidance or cancer diagnosis. This reveals the impact and relevance of the presented work.This project has been funded by MECD (reference AP2009-2414) and US National Institutes of Health (R01 Grant #CA154444), and the US National Science Foundation (III Grant #0916690). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, and NSF. The authors of this manuscript have no conflict of interest with the presented workLago, MA.; Rupérez Moreno, MJ.; Martínez Martínez, F.; Martinez-Sanchis, S.; Bakic, P.; Monserrat, C. (2015). Methodology based on genetic heuristics for in-vivo characterizing the patient-specific biomechanical behavior of the breast tissues. Expert Systems with Applications. 42(21):7942-7950. https://doi.org/10.1016/j.eswa.2015.05.058S79427950422

A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea

The characterization of the mechanical behavior of soft living tissues is a big challenge in Biomechanics. The difficulty arises from both the access to the tissues and the manipulation in order to know their physical properties. Currently, the biomechanical characterization of the organs is mainly performed by testing ex-vivo samples or by means of indentation tests. In the first case, the obtained behavior does not represent the real behavior of the organ. In the second case, it is only a representation of the mechanical response of the indented areas. The purpose of the research reported in this thesis is the development of a methodology to in-vivo characterize the biomechanical behavior of two different organs: the breast and the cornea. The proposed methodology avoids invasive measurements to obtain the mechanical response of the organs and is able to completely characterize of the biomechanical behavior of them. The research reported in this thesis describes a methodology to in-vivo characterize the biomechanical behavior of the breast and the cornea. The estimation of the elastic constants of the constitutive equations that define the mechanical behavior of these organs is performed using an iterative search algorithm which optimizes these parameters. The search is based on the iterative variation of the elastic constants of the model in order to increase the similarity between a simulated deformation of the organ and the real one. The similarity is measured by means of a volumetric similarity function which combines overlap-based coefficients and distance-based coefficients. Due to the number of parameters to be characterized as well as the non-convergences that the solution may present in some regions, genetic heuristics were chosen to drive the search algorithm. In the case of the breast, the elastic constants of an anisotropic hyperelastic neo-Hookean model proposed to simulate the compression of the breast during an MRI-guided biopsy were estimated. Results from this analysis showed that the proposed algorithm accurately found the elastic constants of the proposed model, providing an average relative error below 10%. The methodology was validated using breast software phantoms. Nevertheless, this methodology can be easily transferred into its use with real breasts. In the case of the cornea, the elastic constants of a hyperelastic second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied in order to simulate the deformation of the human corneas due to non-contact tonometry. The iterative search was applied in order to estimate the elastic constants of the model which approximates the most the simulated deformation to the real one. Results showed that these constants can be estimated with an error of about 5%. After the results obtained for both organs, it can be concluded that the iterative search methodology presented in this thesis allows the \textit{in-vivo} estimation the patient-specific elastic constants of the constitutive biomechanical models that govern the biomechanical behavior of these two organs.Lago Ángel, MÁ. (2014). A new approach for the in-vivo characterization of the biomechanical behavior of the breast and the cornea [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/44116TESI

Comparison of different constitutive models to characterize the viscoelastic properties of human abdominal adipose tissue. A pilot study

Knowing the mechanical properties of human adipose tissue is key to simulate surgeries such as liposuction, mammoplasty and many plastic surgeries in which the subcutaneous fat is present. One of the most important surgeries, for its incidence, is the breast reconstruction surgery that follows a mastectomy. In this case, achieving a deformed shape similar to the healthy breast is crucial. The reconstruction is most commonly made using autologous tissue, taken from the patient's abdomen. The amount of autologous tissue and its mechanical properties have a strong influence on the shape of the reconstructed breast. In this work, the viscoelastic mechanical properties of the human adipose tissue have been studied. Uniaxial compression stress relaxation tests were performed in adipose tissue specimens extracted from the human abdomen. Two different viscoelastic models were used to fit to the experimental tests: a quasi-linear viscoelastic (QLV) model and an internal variables viscoelastic (IVV) model; each one with four different hyperelastic strain energy density functions to characterise the elastic response: a 5-terms polynomial function, a first order Ogden function, an isotropic Gasser-Ogden-Holzapfel function and a combination of a neoHookean and an exponential function. The IVV model with the Ogden function was the best combination to fit the experimental tests. The viscoelastic properties are not important in the simulation of the static deformed shape of the breast, but they are needed in a relaxation test performed under finite strain rate, particularly, to derive the long-term behaviour (as time tends to infinity), needed to estimate the static deformed shape of the breast. The so obtained stiffness was compared with previous results given in the literature for adipose tissue of different regions, which exhibited a wide dispersion.Ministerio de Economía y Competitividad DPI2011-2808

Novel applications, model, and methods in magnetic resonance elastography

Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique that maps and quantifies the mechanical properties of soft tissue related to the propagation and attenuation of shear waves. There is considerable interest in whether MRE can bring new insight into pathologies. Brain in particular has been of utmost interest in the recent years. Brain tumors, Alzheimer's disease, and Multiple Sclerosis have all been subjects of MRE studies. This thesis addresses four aspects of MRE, ranging from novel applications in brain MRE, to physiological interpretation of measured mechanical properties, to improvements in MRE technology. First, we present longitudinal measurements of the mechanical properties of glioblastoma tumorigenesis and progression in a mouse model. Second, we present a new finding from our group regarding a localized change in mechanical properties of neural tissue when functionally stimulated. Third, we address contradictory results in the literature regarding the effects of vascular pressure on shear wave speed in soft tissues. To reconcile these observations, a mathematical model based on poro-hyperelasticity is used. Finally, we consider a part of MRE that requires inferring mechanical properties from MR measurements of vibration patterns in tissue. We present improvements to MRE reconstruction methods by developing and using an advanced variational formulation of the forward problem for shear wave propagation

Data-driven patient-specific breast modeling: a simple, automatized, and robust computational pipeline

Background: Breast-conserving surgery is the most acceptable option for breast cancer removal from an invasive and psychological point of view. During the surgical procedure, the imaging acquisition using Magnetic Image Resonance is performed in the prone configuration, while the surgery is achieved in the supine stance. Thus, a considerable movement of the breast between the two poses drives the tumor to move, complicating the surgeon's task. Therefore, to keep track of the lesion, the surgeon employs ultrasound imaging to mark the tumor with a metallic harpoon or radioactive tags. This procedure, in addition to an invasive characteristic, is a supplemental source of uncertainty. Consequently, developing a numerical method to predict the tumor movement between the imaging and intra-operative configuration is of significant interest. Methods: In this work, a simulation pipeline allowing the prediction of patient-specific breast tumor movement was put forward, including personalized preoperative surgical drawings. Through image segmentation, a subject-specific finite element biomechanical model is obtained. By first computing an undeformed state of the breast (equivalent to a nullified gravity), the estimated intra-operative configuration is then evaluated using our developed registration methods. Finally, the model is calibrated using a surface acquisition in the intra-operative stance to minimize the prediction error. Findings: The capabilities of our breast biomechanical model to reproduce real breast deformations were evaluated. To this extent, the estimated geometry of the supine breast configuration was computed using a corotational elastic material model formulation. The subject-specific mechanical properties of the breast and skin were assessed, to get the best estimates of the prone configuration. The final results are a Mean Absolute Error of 4.00 mm for the mechanical parameters E_breast = 0.32 kPa and E_skin = 22.72 kPa. The optimized mechanical parameters are congruent with the recent state-of-the-art. The simulation (including finding the undeformed and prone configuration) takes less than 20 s. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 15 to 100 iterations depending on the initial parameters for a total time comprised between 5 to 30 min. To our knowledge, our model offers one of the best compromises between accuracy and speed. The model could be effortlessly enriched through our recent work to facilitate the use of complex material models by only describing the strain density energy function of the material. In a second study, we developed a second breast model aiming at mapping a generic model embedding breast-conserving surgical drawing to any patient. We demonstrated the clinical applications of such a model in a real-case scenario, offering a relevant education tool for an inexperienced surgeon

A Novel Ultrasound Elastography Technique for Evaluating Tumor Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer

Breast cancer is the second most diagnosed cancer in women, estimated to affect 1 in 8 women during their lifetime. About 10% to 20% of new breast cancer cases are diagnosed with locally advanced breast cancer (LABC). LABC tumors are usually larger than 5 cm and/or attached to the skin or chest wall. It has been reported that when such cases are treated with surgery alone, metastasis and mortality rates are high, especially where skin involvement or attachment to the chest wall is extensive. As such, efficient treatment for this kind of breast cancer includes neoadjuvant chemotherapy (NAC) to shrink the tumor and detach it from the chest wall followed by surgery. Several studies have shown that there is a strong correlation between response to NAC and improved treatment outcomes, including survival rate. Unfortunately, 30% to 40% of patients do not respond to chemotherapy, hence losing critical treatment time and resources. Predicting a patient’s response at the early stages of treatment can help physicians make informed decisions about whether to continue the treatment or use an alternative treatment if a poor response is predicted. Such early and accurate response prediction can shorten the wasted time and reduce resources dedicated to patients while they endure significant side effects. Therefore, it is important to identify this group of non-responder patients as early as possible so that they can be prescribed alternative treatments. Current methods for evaluating LABC response to NAC are based on changes in tumor dimensions using physical examinations or standard anatomical imaging. Such changes may take several months to be detectable. Studies have shown that there is a correlation between LABC response to NAC and tumor softening. In other words, in contrast to responder patients where tumor stiffness generally decreases in response to NAC, in non-responder patients the stiffness of the tumor increases or does not change significantly. As such, a reliable and widely available breast elastography technique can have a major impact on the effective treatment of LABC patients. In this study, we first develop a tissue-mechanics-based method for improving the accuracy of ultrasound elastography. This method consists of 3 steps that are applied to the displacement fields generated from conventional motion-tracking methods. These three steps include: smoothing the displacement fields using Laplacian filtering, enforcing tissue incompressibility equation to refine the displacement fields, and finally enforcing tissue compatibility equation to refine the strain fields. The method was promising through validation using in silico, phantom, and in vivo studies. A huge improvement of this method compared to other motion-tracking methods is its ability in generating lateral displacement with high accuracy. This becomes especially important when the displacement and strain fields are used as inputs to an inverse-problem framework for calculating the stiffness characteristics of tissue, for example, Young’s modulus. We then use this enhanced ultrasound elastography technique to assess the response of LABC patients to NAC based on monitoring the stiffness of their tumors throughout the chemotherapy course. Our results show that this method is effective in predicting patients’ responses accurately as early as 1 week after NAC initiation

Heterogeneous Material Characterization Using Incomplete and Complete Data with Application to Soft Solids

This dissertation proposes and develops novel features into the existing inverse algorithms for characterizing nonhomogeneous material properties of soft solids. Firstly, a new feature that material properties are defined as a piece-wise constant in each element has been implemented in the inverse program. Secondly, to reduce boundary sensitivity of the solution to the inverse problem in elasticity, we modify the objective function using a spatially weighted displacement correlation term. Compared to the conventional objective function, the new formulation performs well in preserving stiffness contrast between the inclusion and background. Then, we present an approach to estimate the nonhomogeneous elastic property distribution using only boundary displacement datasets. We further improve this approach by using force indentation measurements to quantitatively map the elastic properties and analyze the sensitivity of this approach to a variety of factors, e.g., the location and size of the inclusion. Furthermore, we present a method to quantitatively determine the shear modulus distribution using full-field displacements with partially known material properties on the boundary and without any traction or force information. We test its performance using two different types of regularization: total variation diminishing (TVD) and total contrast diminishing (TCD) regularizations. We observe that TCD regularization is capable of mapping the absolute shear modulus distribution, while TVD regularization fails to achieve this. Furthermore, we investigate the feasibility of using the linear elastic inverse solver to solve inverse problems for nonlinear elasticity for large deformations. We conclude that the linear elastic approximation will overestimate the stiffness contrast between the inclusion and background. We also extend the inverse strategy to map the orthotropic linear elastic parameter distributions. The reconstructions reveal that this method performs well in the presence of low displacement noise levels, while performing poorly with 3% noise. Finally, a feature that maps the viscoelastic behavior of solids using harmonic displacement data has been implemented and tested. In summary, these new features not only strengthen our understanding in solving the inverse problem for inhomogeneous material property characterization, but also provide a potential technique to characterize nonhomogeneous material properties of soft tissues nondestructively that could be useful in clinical practice