Lung cancer is by far the leading cause of cancer death among both men and women; according to the American Cancer Society, approximately 1 out of 4 cancer deaths are due to lung cancer. The primary treatment for the condition generally involves External Beam Radiation Therapy (EBRT). Lung cancer tumour motion is generally clinically significant and presents a major challenge for clinicians. With significant lung tumour motion (>;5mm) during respiration comes the requirement for motion compensation techniques1 . Ideally, continuous real-time tumour tracking allows for continuous radiation delivery such that the tumour receives sufficient radiation dose while minimizing dose to surrounding healthy lung tissue. Direct tumour tracking is often not possible in non-contrast images and a surrogate is required for tumour motion. Among surrogates for tumour tracking, the diaphragm muscle has shown to provide good correlation with tumour motion2 . Motion compensation techniques often require extensive 4D CT scans which is inherently dangerous. The diaphragm muscle, the major driver of respiratory motion, can also be incorporated into lung biomechanical models used to predict deformations in the lungs and surrounding organs during respiration3 . This research involves the development of a patient specific biomechanical model of the diaphragm muscle with both passive and active responses. Detailed anatomical, and geometric information, including the muscle micromechanics, is used to generate a Finite Element Model (FEM) of the diaphragm in order to predict its in vivo motion. Results from modelling a patient specific case revealed a good match between the simulated and actual contracted diaphragm surface with an average mean squared difference of 2.83 mm.
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.