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The diaphragm plays a crucial role in respiration, and diaphragm dysfunction is common in COPD, contributing to worsening symptoms and higher mortality rates. Many methods have been implemented to evaluate the function of the diaphragm, but each comes with its unique set of limitations. To overcome this challenge, a novel approach was introduced to assess diaphragm function in patients with chronic obstructive pulmonary disease (COPD) using thoracic computed tomography scans. This novel approach involves generating a simulation of the diaphragm from lung DICOM slice images and calculating the dynamic change in respiratory motion by quantifying the difference in the simulated diaphragm mesh area during inhalation and exhalation. The experimental design incorporates various image processing algorithms, computational geometry algorithms, and a surface fitting algorithm optimized to minimize the potential sources of error. When the proposed technique was applied to detect diaphragmatic dysfunction in patients with COPD, the results of the Pearson correlation analysis showed a strong relationship between the variables ratio of exhalation to inhalation surface and average z-value difference inhalation and exhalation surfaces, with a coefficient of 0.731. The results suggest that the proposed technique is highly accurate and beneficial in scenarios where diaphragm function is essential, such as respiratory disorders, neuromuscular diseases like Amyotrophic Lateral Sclerosis or muscular dystrophy, spinal cord injuries, and conditions such as sleep apnea. By providing specific numerical data on sensitivity and specificity, this approach offers a quantitative evaluation of its effectiveness in detecting diaphragmatic dysfunction in patients with COPD. In conclusion, the proposed approach is currently semi-automated, however, future study may investigate fully automated approaches using this technique.

Motility Analysis of Diaphragm in Patients with Chronic Pulmonary Lung Disease Based on Computed Tomography Technique