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

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

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, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Mechanical Characterization of Soft Materials Using Volume-Controlled Cavitation

The mechanical properties of soft materials are used in a wide range of fields and applications including biomedical engineering, sports, and automobile industry, in addition to medical applications. Therefore, several methods have been used to measure these properties including tension, compression, and indentation. This study focuses on the application of multiaxial loading using cavitation mechanics to measure nonlinear mechanical properties of soft materials. It was found that applying controlled cavitation within the internal structure of soft materials provided enough information to characterize their mechanical behavior. This is done by inserting a needle-balloon tool inside the tested material while being attached to a system that allows for injections of an incompressible fluid (water) into the balloon. To establish this methodology as a robust characterization technique of the mechanics of soft materials, it was used in a four-stages investigation: developing an analytical framework to characterize the non-linear elastic behavior of rubber-like materials (elastomeric gels), measuring the hyperelastic properties of soft biological tissues (porcine liver), comparing the cavity expansion test with a conventional uniaxial tensile testing, and establishing an analytical framework to characterize the time-dependent behavior of viscoelastic materials. In the first stage, a solution that relates the applied radial loads and tangential deformation is introduced. This solution allows the calibration of hyperelastic strain energy functions (SEF), which were Yeoh, Arruda-Boyce and Ogden (used in all stages). Finite element simulations were used to validate the material parameters of the three hyperelastic models. Computed tomography (CT) imaging was used to validate the spherical configuration assumption of the inflated balloon inside the sample. The validation process considered the two types of stresses generated during the test, radial and hoop stresses. It was observed that the radial stresses were insignificant compared to the hoop stresses. In the second stage, a smaller balloon was used to test porcine liver tissues; however, the protocol of this stage was similar to the first stage. Few changes were introduced to the definition of the deformation term, as a result, the measured deformations in the cavity test coincided with the deformation levels reported in literature. In addition, the three hyperelastic models predicted initial shear moduli that agreed with their counterparts reported in literature using conventional testing techniques. To understand the similarities and differences between the cavity expansion test and conventional axial loading, the third stage addressed the comparison between the cavitation and uniaxial tension characterization. The comparison focused on the stress levels, range of strains as well as the initial shear moduli. It was found that the strain levels in the hydrogels were similar up to the failure point. In addition, the hoop stresses generated due to cavity loads were similar to the tensile stresses generated in uniaxial tension up to a strain level of 45%. Afterward, hoop stresses increased exponentially reaching a peak magnitude that was twice that observed in the uniaxial tension. Since the radial stresses were insignificant, the previous two observations provided an indication to the equi-biaxial nature of the cavity expansion test. The final stage of this study addressed the characterization of the viscoelastic properties of rubber-like materials. In this stage, linear viscoelastic theory was used. The cavitation rheology is used to measure the non-linear elastic response of the hydrogels at three different strain rates. The simple shear relaxation test was used to measure the viscous response of the hydrogels. While the elastic material parameters were calibrated using the same method used in previous stages, the viscous coefficients of the Prony series were determined using Abaqus’ calibration tool. Afterward, the elastic parameters and viscous coefficients were used to reproduce the experimental data numerically using FE simulations, and analytically using Matlab code. The agreement between experimental data, FE simulations and the analytical code showed that the cavity expansion test was capable of measuring the time-dependent response of rubber-like materials