Surface deformation analysis of collapsed lungs using model-based shape matching

Area of Science:

• Medical Imaging
• Computational Anatomy
• Surgical Navigation

Background:

• Intraoperative localization of lung nodules is challenging.
• Pneumothorax induces significant three-dimensional surface deformation in the lung.
• Accurate modeling of these deformations is crucial for surgical guidance.

Purpose of the Study:

• To develop and validate a model-based shape matching technique for analyzing lung surface deformation during pneumothorax.
• To improve the accuracy of intraoperative localization of lung nodules.

Main Methods:

• Utilized contrast-enhanced computed tomography (CT) images of beagle dog lungs at varying bronchial pressures.
• Developed a Laplacian-based shape matching solution to optimize differential displacement fields for large deformations and pixel intensity shifts.
• Applied the technique to analyze inter-subject three-dimensional surface deformation.

Main Results:

• Demonstrated improved registration accuracy compared to existing shape matching methods.
• Achieved enhanced shape similarity and reduced target displacement errors.
• Visualized spatial displacement of the lung surface with an average error within 5 mm.

Conclusions:

• The proposed method effectively addresses challenges in matching surfaces with significant curvature and deformation.
• Achieved superior registration accuracy, particularly in critical regions.
• Provides a robust framework for pneumothorax deformation modeling and enhances surgical planning.