Biomechanical Modeling for Lung Tumor Motion Prediction during Brachytherapy and Radiotherapy

A novel technique is proposed to develop a biomechanical model for estimating lung’s tumor position as a function of respiration cycle time. Continuous tumor motion is a major challenge in lung cancer treatment techniques where the tumor needs to be targeted; e.g. in external beam radiotherapy and brachytherapy. If not accounted for, this motion leads to areas of radiation over and/or under dosage for normal tissue and tumors. In this thesis, biomechanical models were developed for lung tumor motion predication in two distinct cases of lung brachytherapy and lung external beam radiotherapy. The lung and other relevant surrounding organs geometries, loading, boundary conditions and mechanical properties were considered and incorporated properly for each case. While using material model with constant incompressibility is sufficient to model the lung tissue in the brachytherapy case, in external beam radiation therapy the tissue incompressibility varies significantly due to normal breathing. One of the main issues tackled in this research is characterizing lung tissue incompressibility variations and measuring its corresponding parameters as a function of respiration cycle time. Results obtained from an ex-vivo porcine deflated lung indicated feasibility and reliability of using the developed biomechanical model to predict tumor motion during brachytherapy. For external beam radiotherapy, in-silico studies indicated very significant impact of considering the lung tissue incompressibility on the accuracy of predicting tumor motion. Furthermore, ex-vivo porcine lung experiments demonstrated the capability and reliability of the proposed approach for predicting tumor motion as a function of cyclic time. As such, the proposed models have a good potential to be incorporated effectively in computer assisted lung radiotherapy treatment systems

Doctor of Philosophy

dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload

A Composite Material-based Computational Model for Diaphragm Muscle Biomechanical Simulation

Lung cancer is the most common cause of cancer related death among both men and women. Radiation therapy is the most widely used treatment for this disease. Motion compensation for tumor movement is often clinically important and biomechanics-based motion models may provide the most robust method as they are based on the physics of motion. In this study, we aim to develop a patient specific biomechanical model that predicts the deformation field of the diaphragm muscle during respiration. The first part of the project involved developing an accurate and adaptable micro-to-macro mechanical approach for skeletal muscle tissue modelling for application in a FE solver. The next objective was to develop the FE-based mechanical model of the diaphragm muscle based on patient specific 4D-CT data. The model shows adaptability to pathologies and may have the potential to be incorporated into respiratory models for the aid in treatment and diagnosis of diseases

Multi-scale mechanics of collagen extracellular matrix

Extracellular matrix (ECM) provides the principal avenue for mechanochemical communication between tissue and cells. ECM not only plays an important role in providing structural and biomechanical support, but also in regulating a series of cellular behaviors. However, the underlying mechanisms by which the ECM mechanics influence cell and tissue function remain to be elucidated, since this process span size scales from tissue to molecular level. Furthermore, ECM has a hierarchical 3D structure and the load distribution is highly dependent on the architecture and mechanical properties. In this thesis, the multiscale mechanics of collagen ECM was studied using both experimental and modeling approaches. Rheological and biaxial tensile testing were performed to study the macroscopic mechanical properties. A theoretical framework was developed to determine the continuous relaxation spectrum. Investigating the spectrum in terms of number of peaks, peak intensity and time constants sheds light on the main intrinsic properties of viscoelastic materials. Material parameters from continuous relaxation spectrum were used in finite element models to simulate the dynamic rheological measurements of collagen matrix. The microscopic mechanical properties were measured using Optical Magnetic Twisting Cytometry (OMTC). Ferromagnetic beads embedded in the matrix were used as mechanical probes. Our study on the macro- and micro-scopic mechanical properties of collage matrix suggested several interesting differences originated from the scales of measurements. In macroscopic measurements, the storage and shear modulus increase with collagen concentration. At microscopic scale, the apparent storage and loss modulus are less sensitive to changes in collagen concentration. However, the loss modulus is more affected by the local interstitial fluid environment, leading to an increase in viscosity, especially at higher frequencies. A novel experimental approach was established to study the multi-scale ECM mechanics that allows the measurements of local ECM mechanical properties with controlled tissue-level mechanical loading by integrating the OMTC and biaxial tensile tester. Multiphoton imaging reveals structural changes in the collagen network that involve gradual straightening and collagen fiber recruitment with tissue level mechanical loading. Our study shows there is a complex interplay among structural heterogeneity, collagen fiber orientation, and fiber engagement in determining the ECM local mechanical properties

Modelling studies on biological tissue properties and mechanical responses under external stimuli

PhDBiological tissues maintain their homeostasis by remodelling under external mechanical stimuli. In order to understand the tissue remodelling process, it is important to characterize tissue properties before detailed mechanical responses can be investigated. This project aims to develop a computational modelling framework to characterise mechanical properties of biological tissues, and to quantify tissue responses under mechanical loading. The thesis presents, first, mechanical responses of articular cartilages under different loadings using a poroelastic model. Unique in this study, collagen fibrils are treated separately from the rest of ECM, as they only resists tension. This leads to a fibril-reinforced poroelastic model. Effects of the distribution of the collagen fibrils and their orientation on tissue mechanical responses are investigated. Most of the effort has been on the mechanical stress distribution of the human left atrium and its correlation to electrophysiology patterns in atrial fibrillation. Detailed mechanical responses of the atrial wall to a step pressure increase in the left atrium are calculated. The geometry of the left atrium is based on patient specific images using cardio CT and incorporates variations of the atrial wall thickness as well as unique fibre orientation patterns. We hypothesize that areas of high von Mises stress are correlated to foci of abnormal electrophysiology sites which sustain cardiac arrhythmia. Results from this study show a positive correlation between them. To our knowledge, this is the first study that establishes the relationship between the atrial wall stress distribution and the atrial abnormal electrophysiology sites. The project also investigates hyperelastic properties of endothelial cells and the overlying endothelial glycocalyx, based on data from AFM micro-indentation. Both endothelial cells with & without the glycocalyx layer (i.e. following enzymatic digestion) are used. This is the first time that the mechanical property of the glycocalyx is estimated using an inverse biomechanical model

Optimization of Indentation for the Material Characterization of Soft PVA-Cryogels

Over the past few years a variety of clinical procedures aiming at tissue repair and other relevant therapies have been under active investigation [12,32]. Success of procedures aimed at soft tissue repair depend on the combined response of biochemical and biomechanical properties of the organs neighbouring the tissue [53]. Using human or animal cadaveric tissue for this purpose is very challenging due to issues pertaining to biodegradability and infection or biohazard risk factors [135,205].As such, tissue mimicking materials (e.g. Polyvinyl Alcohol Cryo-gel (PVA-C)) have been investigated to satisfy the need for the said clinical applications. Advantages of using tissue-mimicking materials include (a) biocompatibility, (b) being not biodegradable and long term shape preservation and (c) having similar biomechanical properties of human tissue [76,126]. To assess biomechanical compatibility of tissue mimicking materials, various mechanical testing techniques have been proposed. Among them, indentation testing has shown great potential for this purpose and it has been used broadly for tissue biomechanical characterization [158]. This method has become more popular because it allows for cost effective, non-destructive, quick, and quantitative assessment of soft tissue biomechanics [64,193]. Soft tissue is idealized as non-linear [46], isotropic [72] and incompressible [198] material. Given its interesting properties and biocompatibility, PVA-C has attracted a great deal of attention as a biocompatible material suitable for clinical applications such as tissue repair, tissue engineering etc. As such, many studies have been conducted to understand this material’s mechanical properties and its suitability for fabricating artificial cornea replacement [54], heart valve [90], lung [164], breast [167], kidney [169], brain [195], stomach [160], bladder [18], prostate [36] and articular cartilage [20] This stems from that this material has similar characteristics to human soft tissue [44,46,129]. Similar to biological tissues, the internal structure of PVA-C leads to nonlinear behavior [66, 80]. This nonlinearity becomes predominant while it undergoes large deformation [205]. Several analytical, semi-analytical and computational models have been proposed to understand tissue mechanical behavior, including its linear and nonlinear behavior, under indentation testing[60]. These include the methods proposed by Boussinesq [27],Sneddon[176],Hayes[77], Cao [34]. This thesis aims at gaining in-depth insight into the mechanical behavior of PVA-C under indentation testing. To this end it presents development of an inverse Finite Element (FE) techniques solved using numerical optimization to characterize the mechanical properties of PVA-C specimens. used to understand the indentation response of PVA-C at different thickness and conditions. The investigation reported in this thesis includes numerical analysis where displacement influence factor was employed in conjunction with linear elastic model of finite thickness. In the analysis, effects of Poisson’s ratio, specimen aspect ratio and relative indentation depth were investigated and a novel mathematical term was introduced to Sneddon’s equation. Results indicate that the developed models have been successful to characterize PVA-C material while they can be used effectively in characterizing the mechanical behavior of biological tissue specimens obtained from medical intervention

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

Finite element modeling of soft tissue deformation.

Computer-aided minimally invasive surgery (MIS) has progressed significantly in the last decade and it has great potential in surgical planning and operations. To limit the damage to nearby healthy tissue, accurate modeling is required of the mechanical behavior of a target soft tissue subject to surgical manipulations. Therefore, the study of soft tissue deformations is important for computer-aided (MIS) in surgical planning and operation, or in developing surgical simulation tools or systems. The image acquisition facilities are also important for prediction accuracy. This dissertation addresses partial differential and integral equations (PDIE) based biomechanical modeling of soft tissue deformations incorporating the specific material properties to characterize the soft tissue responses for certain human interface behaviors. To achieve accurate simulation of real tissue deformations, several biomechanical finite element (FE) models are proposed to characterize liver tissue. The contribution of this work is in theoretical and practical aspects of tissue modeling. High resolution imaging techniques of Micro Computed Tomography (Micro-CT) and Cone Beam Computed Tomography (CBCT) imaging are first proposed to study soft tissue deformation in this dissertation. These high resolution imaging techniques can detect the tissue deformation details in the contact region between the tissue and the probe for small force loads which would be applied to a surgical probe used. Traditional imaging techniques in clinics can only achieve low image resolutions. Very small force loads seen in these procedures can only yield tissue deformation on the few millimeters to submillimeter scale. Small variations are hardly to detect. Furthermore, if a model is validated using high resolution images, it implies that the model is true in using the same model for low resolution imaging facilities. The reverse cannot be true since the small variations at the sub-millimeter level cannot be detected. In this dissertation, liver tissue deformations, surface morphological changes, and volume variations are explored and compared from simulations and experiments. The contributions of the dissertation are as follows. For liver tissue, for small force loads (5 grams to tens of grams), the linear elastic model and the neo-Hooke\u27s hyperelastic model are applied and shown to yield some discrepancies among them in simulations and discrepancies between simulations and experiments. The proposed finite element models are verified for liver tissue. A general FE modeling validation system is proposed to verify the applicability of FE models to the soft tissue deformation study. The validation of some FE models is performed visually and quantitatively in several ways in comparison with the actual experimental results. Comparisons among these models are also performed to show their advantages and disadvantages. The method or verification system can be applied for other soft tissues for the finite element analysis of the soft tissue deformation. For brain tissue, an elasticity based model was proposed previously employing local elasticity and Poisson\u27s ratio. It is validated by intraoperative images to show more accurate prediction of brain deformation than the linear elastic model. FE analysis of brain ventricle shape changes was also performed to capture the dynamic variation of the ventricles in author\u27s other works. There, for the safety reasons, the images for brain deformation modeling were from Magnetic Resonance Imaging (MRI) scanning which have been used for brain scanning. The measurement process of material properties involves the tissue desiccation, machine limits, human operation errors, and time factors. The acquired material parameters from measurement devices may have some difference from the tissue used in real state of experiments. Therefore, an experimental and simulation based method to inversely evaluate the material parameters is proposed and compare

A Heterogeneous Patient-Specific Biomechanical Model of the Lung for Tumor Motion Compensation and Effective Lung Radiation Therapy Planning

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies