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Micromechanics based modelling of in-vivo respiratory motion of the diaphragm muscle with the incorporation of optimized z-disks mechanics
Author(s): Brett Coelho; Abbas Samani
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Paper Abstract

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.

Paper Details

Date Published: 13 March 2018
PDF: 6 pages
Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105762U (13 March 2018); doi: 10.1117/12.2293846
Show Author Affiliations
Brett Coelho, Western Univ. (Canada)
Abbas Samani, Western Univ. (Canada)
Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 10576:
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Robert J. Webster, Editor(s)

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