Lung cancer radiotherapy is prone to errors due to uncertainties caused by the respiratory motion. If not accounted for, these errors may lead to poor radiation dose distribution, including insufficient does to the tumor volume and excessive dose to the healthy lung parenchyma. One effective method to account for respiratory motion is motion modeling. In this paper, we present a hybrid motion model which consists of two parts: 1) a computational biomechanical model of the lung for real-time tumor location/deformation estimation and 2) a Neural Network (NN) for real-time estimation of loading and boundary conditions of the lung biomechanical model. The second part uses the chest and abdomen surface motion as surrogate for the loading and boundary conditions, and is the main driver of the lung’s biomechanical model of the lung. In practice, the tumor location/deformation data estimated using the proposed motion model can be fed to actuators that guide a radiation therapy LINAC for continuous lung tumor targeting. The focus of this paper is two-fold: 1) developing two NNs for predicting the lung BC’s, including the diaphragm motion and transpulmonary pressure and 2) incorporating the NNs into a previously developed lung FE model to determine tumor location/deformation. Results of these two steps show highly favorable accuracy of the NNs in estimating the lung BC’s and highly favorable accuracy of the proposed motion model in predicting the lung tumor motion. As such, the proposed tracking approach can be potentially used for managing lung respiratory motion/deformation necessary for effective EBRT.
Diverse cardiac conditions such as myocardial infarction and hypertension can lead to diastolic dysfunction as a prevalent cardiac condition. Diastolic dysfunctions can be diagnosed through different adverse mechanisms such as abnormal left ventricle (LV) relaxation, filling, and diastolic stiffness. This paper is geared towards evaluating diastolic stiffness and measuring the LV blood pressure non-invasively. Diastolic stiffness is an important parameter which can be exploited for more accurate diagnosis of diastolic dysfunction. For this purpose, a finite element (FE) LV mechanical model, which works based on a novel composite material model of the cardiac tissue, was utilized. Here, this model was tested for inversion-based applications where it was applied for estimating the cardiac tissue passive stiffness mechanical properties as well as diastolic LV blood pressure. To this end, the model was applied to simulate diastolic inflation of the human LV. The start-diastolic LV geometry was obtained from MR image data segmentation of a healthy human volunteer. The obtained LV geometry was discretized into a FE mesh before FE simulation was conducted. The LV tissue stiffness and diastolic LV blood pressure were adjusted through optimization to achieve the best match between the calculated LV geometry and the one obtained from imaging data. The performance of the LV mechanical simulations using the optimal values of tissue stiffness and blood pressure was validated by comparing the geometrical parameters of the dilated LV model as well as the stress and strain distributions through the LV model with available measurements reported on the LV dilation.
According to statistics, lung disease is among the leading causes of death worldwide. As such, many research groups are developing powerful tools for understanding, diagnosis and treatment of various lung diseases. Recently, biomechanical modeling has emerged as an effective tool for better understanding of human physiology, disease diagnosis and computer assisted medical intervention. Mechanical properties of lung tissue are important requirements for methods developed for lung disease diagnosis and medical intervention. As such, the main objective of this study is to develop an effective tool for estimating the mechanical properties of normal and pathological lung parenchyma tissue based on its microstructure. For this purpose, a micromechanical model of the lung tissue was developed using finite element (FE) method, and the model was demonstrated to have application in estimating the mechanical properties of lung alveolar wall. The proposed model was developed by assembling truncated octahedron tissue units resembling the alveoli. A compression test was simulated using finite element method on the created geometry and the hyper-elastic parameters of the alveoli wall were calculated using reported alveolar wall stress-strain data and an inverse optimization framework. Preliminary results indicate that the proposed model can be potentially used to reconstruct microstructural images of lung tissue using macro-scale tissue response for normal and different pathological conditions. Such images can be used for effective diagnosis of lung diseases such as Chronic Obstructive Pulmonary Disease (COPD).
Lung cancer is one of the leading causes of cancer death in men and women. External Beam Radiation Therapy (EBRT) is a commonly used primary treatment for the condition. A major challenge with such treatments is the delivery of sufficient radiation dose to the lung tumor while ensuring that surrounding healthy lung parenchyma receives only minimal dose. This can be achieved by coupling EBRT with respiratory computer models which can predict the tumour location as a function of phase during the breathing cycle1. The diaphragm muscle contraction is mainly responsible for a large portion of the lung tumor motion during normal breathing, especially when tumours are in the lower lobes, therefore the importance of accurately modelling the diaphragm is paramount in lung tumour motion prediction. The goal of this research is to develop a biomechanical model of the diaphragm, including its active and passive response, using detailed geometric, biomechanical and anatomical information that mimics the diaphragmatic behaviour in a patient specific manner. For this purpose, a Finite Element Model (FEM) of the diaphragm was developed in order to predict the in vivo motion of the diaphragm, paving the way for computer assisted lung cancer tumor tracking in EBRT. Preliminary results obtained from the proposed model are promising and they indicate that it can be used as a plausible tool for effective lung cancer EBRT to improve patient care.
In-depth understanding of the diaphragm’s anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm’s lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm’s easy-to-segment contacting organs—including the lungs, heart, aorta, and ribcage—to guide the diaphragm’s segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is 2.55±0.39 mm, which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology.
Despite recent advances in image-guided interventions, lung cancer External Beam Radiation Therapy (EBRT) is still very challenging due to respiration induced tumor motion. Among various proposed methods of tumor motion compensation, real-time tumor tracking is known to be one of the most effective solutions as it allows for maximum normal tissue sparing, less overall radiation exposure and a shorter treatment session. As such, we propose a biomechanics-based real-time tumor tracking method for effective lung cancer radiotherapy. In the proposed algorithm, the required boundary conditions for the lung Finite Element model, including diaphragm motion, are obtained using the chest surface motion as a surrogate signal. The primary objective of this paper is to demonstrate the feasibility of developing a function which is capable of inputting the chest surface motion data and outputting the diaphragm motion in real-time. For this purpose, after quantifying the diaphragm motion with a Principal Component Analysis (PCA) model, correlation coefficient between the model parameters of diaphragm motion and chest motion data was obtained through Partial Least Squares Regression (PLSR). Preliminary results obtained in this study indicate that the PCA coefficients representing the diaphragm motion can be obtained through chest surface motion tracking with high accuracy.
Lung Cancer is the leading cause of cancer death in both men and women. Among various treatment methods currently being used in the clinic, External Beam Radiation Therapy (EBRT) is used widely not only as the primary treatment method, but also in combination with chemotherapy and surgery. However, this method may lack desirable dosimetric accuracy because of respiration induced tumor motion. Recently, biomechanical modeling of the respiratory system has become a popular approach for tumor motion prediction and compensation. This approach requires reasonably accurate data pertaining to thoracic pressure variation, diaphragm position and biomechanical properties of the lung tissue in order to predict the lung tissue deformation and tumor motion. In this paper, we present preliminary results of an in vivo study obtained from a Finite Element Model (FEM) of the lung developed to predict tumor motion during respiration.
The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.
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