A biomechanical approach for in vivo diaphragm muscle motion prediction during normal respiration

Brett Coelho; Elham Karami; Seyyed M. H. Haddad; Behzad Seify; Abbas Samani

doi:10.1117/12.2254590

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3 March 2017 A biomechanical approach for in vivo diaphragm muscle motion prediction during normal respiration

Brett Coelho, Elham Karami, Seyyed M. H. Haddad, Behzad Seify, Abbas Samani

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Proceedings Volume 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling; 1013505 (2017) https://doi.org/10.1117/12.2254590
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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.

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Brett Coelho, Elham Karami, Seyyed M. H. Haddad, Behzad Seify, and Abbas Samani "A biomechanical approach for in vivo diaphragm muscle motion prediction during normal respiration", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 1013505 (3 March 2017); https://doi.org/10.1117/12.2254590

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