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

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 3D discrete model of the diaphragm and human trunk

In this paper, a 3D discrete model is presented to model the movements of the trunk during breathing. In this model, objects are represented by physical particles on their contours. A simple notion of force generated by a linear actuator allows the model to create forces on each particle by way of a geometrical attractor. Tissue elasticity and contractility are modeled by local shape memory and muscular fibers attractors. A specific dynamic MRI study was used to build a simple trunk model comprised of by three compartments: lungs, diaphragm and abdomen. This model was registered on the real geometry. Simulation results were compared qualitatively as well as quantitatively to the experimental data, in terms of volume and geometry. A good correlation was obtained between the model and the real data. Thanks to this model, pathology such as hemidiaphragm paralysis can also be simulated.Comment: published in: "Lung Modelling", France (2006

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

Updated Perspectives on the Role of Biomechanics in COPD: Considerations for the Clinician

Patients with chronic obstructive pulmonary disease (COPD) demonstrate extra-pulmonary functional decline such as an increased prevalence of falls. Biomechanics offers insight into functional decline by examining mechanics of abnormal movement patterns. This review discusses biomechanics of functional outcomes, muscle mechanics, and breathing mechanics in patients with COPD as well as future directions and clinical perspectives. Patients with COPD demonstrate changes in their postural sway during quiet standing compared to controls, and these deficits are exacerbated when sensory information (eg, eyes closed) is manipulated. If standing balance is disrupted with a perturbation, patients with COPD are slower to return to baseline and their muscle activity is differential from controls. When walking, patients with COPD appear to adopt a gait pattern that may increase stability (eg, shorter and wider steps, decreased gait speed) in addition to altered gait variability. Biomechanical muscle mechanics (ie, tension, extensibility, elasticity, and irritability) alterations with COPD are not well documented, with relatively few articles investigating these properties. On the other hand, dyssynchronous motion of the abdomen and rib cage while breathing is well documented in patients with COPD. Newer biomechanical technologies have allowed for estimation of regional, compartmental, lung volumes during activity such as exercise, as well as respiratory muscle activation during breathing. Future directions of biomechanical analyses in COPD are trending toward wearable sensors, big data, and cloud computing. Each of these offers unique opportunities as well as challenges. Advanced analytics of sensor data can offer insight into the health of a system by quantifying complexity or fluctuations in patterns of movement, as healthy systems demonstrate flexibility and are thus adaptable to changing conditions. Biomechanics may offer clinical utility in prediction of 30-day readmissions, identifying disease severity, and patient monitoring. Biomechanics is complementary to other assessments, capturing what patients do, as well as their capability

A Free-Breathing Lung Motion Model

Lung cancer has been the leading cause of cancer deaths for decades in the United States. Although radiotherapy is one of the most effective treatments, side effects from error in delivery of radiation due to organ motion during breathing remain a significant issue. To compensate the breathing motion during the treatment, a free breathing lung motion model, x= x0+αv+βf, was developed and discussed, where x is the position of a piece of tissue located at reference position x0. α is a parameter which characterizes the motion due to local air filling: motion as a function of tidal volume) and β is the parameter that accounts for the motion due to the imbalance of dynamical stress distributions during inspiration and exhalation which cause lung motion hysteresis: motion as a function of airflow). The parameters α and β together provide a quantitative characterization of breathing motion that inherently includes the complex hysteresis interplay. The theoretical foundation of the model was built by investigating the stress distribution inside of a lung and the biomechanical properties of the lung tissues. Accuracy of the model was investigated by using 49 free-breathing patient data sets. Applications of the model in localizing lung cancer, monitoring radiation damage and suppressing artifacts in free-breathing PET images were also discussed. This work supported in part by NIHR01CA096679 and NIHR01CA11671

Inverse-Consistent Determination of Young\u27s Modulus of Human Lung

Human lung undergoes respiration-induced deformation due to sequential inhalation and exhalation. Accurate determination of lung deformation is crucial for tumor localization and targeted radiotherapy in patients with lung cancer. Numerical modeling of human lung dynamics based on underlying physics and physiology enables simulation and virtual visualization of lung deformation. Dynamical modeling is numerically complicated by the lack of information on lung elastic behavior, structural heterogeneity as well as boundary constrains. This study integrates physics-based modeling and image-based data acquisition to develop the patient-specific biomechanical model and consequently establish the first consistent Young\u27s modulus (YM) of human lung. This dissertation has four major components: (i) develop biomechanical model for computation of the flow and deformation characteristics that can utilize subject-specific, spatially-dependent lung material property; (ii) develop a fusion algorithm to integrate deformation results from a deformable image registration (DIR) and physics-based modeling using the theory of Tikhonov regularization; (iii) utilize fusion algorithm to establish unique and consistent patient specific Young\u27s modulus and; (iv) validate biomechanical model utilizing established patient-specific elastic property with imaging data. The simulation is performed on three dimensional lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of human subjects. The heterogeneous Young\u27s modulus is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The biomechanical model adequately predicts the spatio-temporal lung deformation, consistent with data obtained from imaging. The accuracy of the numerical solution is enhanced through fusion with the imaging data beyond the classical comparison of the two sets of data. Finally, the fused displacement results are used to establish unique and consistent patient-specific elastic property of the lung

INVESTIGATING THE METABOLIC PROFILE OF RUN-UP RACES AND THE MECHANICS OF WOBBLING VISCERAL MASS IN VERTICAL JUMPS.

This Thesis is organized in two parts based on two different investigations about human motion: the metabolic and mechanical analysis of \u2018skyscraper running\u2019, and the estimation of the visceral mass displacement in vertical jumps. Skyscraper running is a novel sport activity, in which the athletes run on emergency stairs of the tallest building of the world, during the \u2018run up\u2019 races of the world championship circuit. In PART I of this Thesis, this topic has been analysed in terms of mechanical and metabolic requirements, both at general and individual level. Skyscraper runners\u2019metabolic profile, has been inferred from the total mechanical power estimated in 36 world records (48-421 m tall buildings), ranked by gender and age range. Individual athlete\u2019s performance (n=13) has been experimentally investigated during the Pirelli Vertical Sprint, with data loggers for altitude and heart rate. At a general level, a non-linear regression of Wilkie\u2019s model relating maximal mechanical power to event duration, revealed the gender and age differences in term of maximum aerobic power and anaerobic energy resources particularly needed at the beginning of the race. The total mechanical power was found to be partitioned among: the fraction devolved to raise the body centre of mass: W\u2d9 STA.EXT = 80.4 \ub1 2.9%, the need to accelerate the limbs with respect to the body: W\u2d9 STA.INT = 4.5 \ub1 2.1%, and running in turns between flights of stairs: W\u2d9 TUR =15.1 \ub1 2.0%. At the individual level, experiments revealed that these athletes show a metabolic profile similar to middle-distance runners. Furthermore, best skyscraper runners keep constant vertical speed and heart rate throughout the race, while others suddenly decelerate, negatively affecting the race performance. In PART II of this Thesis another interesting study has been discussed: the mechanics of visceral mass motion in vertical jumps. This internal mass motion could occur in all the locomotion paradigms, and also in all the movements characterized by a high centre of mass vertical displacement. Moreover, visceral mass shows significant couplings with the respiratory system, as has been discussed in the past in famous studies on quadruped locomotion. Here viscera motion has been analyzed in a simple and well know motor task as the vertical jump, focusing on the effect of respiratory and muscle contractions strategies to limit its displacement, and to improve trunk-pelvis segment stiffness. A validated method for the estimation of visceral mass displacement has been applied during jump sequences with two different techniques: six subjects before and after a specific training period, executed the natural jump and the \u201ccontrolled\u201d jump sessions. In that method, the simultaneous measurement of ground reaction forces and spatial coordinates allow the estimation of the relative movement between the \u2018invisible\u2019 abdomen content and the \u2018container\u2019, i.e. the rest of the body as described by the position of external markers. The results show a significantly higher (p < 0.05 \u2013 paired t-test) mean of visceral mass displacement (Total = 0.087 \ub1 s.d. 0.021 m) of all the subjects, in normal jumps, compared to the mean of visceral mass displacement (Total = 0.070 \ub1 s.d. 0.027 m) in controlled jumps. An analysis of variance (ANOVA 2-ways) shows a significant effect of jump technique but also of subject and jump-subject interaction, confirming an elevated variability between the subjects. A intraclass-correlation exhibit a significant pattern (ICC= 0.791; p = 0.017) and in 5 of 6 subjects, there is a higher mean nominal value of VMD in normal jumps. Also pectorals and low abdominal fat displacements has been measured, showing mean values (weighted by a scaling factor) of 4.5 710-4 m and 8.9 710-4 m in normal jumps, and 4.5 710-4 m and 9.6 710-4 m in controlled jumps respectively. A quantitative and qualitative analysis on visceral mass displacement curve has been completed for both the jumping techniques: a comparison with the \u2018periodic\u2019 curve of body centre of mass show a constant delay (\u2018phase shift\u2019) with a mean value of 18.1 \ub1 s.d. 5.73 ms during the aerial phase and 18.8 \ub1 s.d. 9.8 ms in the landing phase. Finally a preliminary estimation of the internal mass vibration parameters has been showed: the mean values and s.d. of the stiffness in normal and controlled jumps are k1= 18.2 \ub1 13.5 KN/m and k2= 17.9 \ub1 12.1 KN/m respectively, while the damping constant mean values and s.d. are c1 300.3 \ub1 170.7 N/(m/s) is and c2 is 287.3 \ub1 129.8 N/(m/s). For the first time, a method for the estimation of visceral mass displacement, useful in biomechanics and in locomotion-respiratory coupling investigations, has been used in an applied condition. The effects of the \u201ccontrolled\u201d jumping techniques using respiration and muscles contraction strategies to limit viscera displacement has been demonstrated. The displacement of visceral mass and the body frame have been quantified and compared, and a preliminary estimation of vibration parameter of the internal system has been showed. We foresee an increasing interest in sports biomechanics to improve athletes jumping performance, as well as in the energetics and biomechanics of locomotion

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