Ventilation Series Similarity: A Study for Ventilation Calculation Using Deformable Image Registration and 4DCT to...

Area of Science:

• Medical Imaging
• Pulmonary Function Analysis
• Computational Anatomy

Background:

• Ventilation distribution calculations using Deformable Image Registration (DIR) and 4D Computed Tomography (4DCT) are crucial for pulmonary studies.
• Motion artifacts in 4DCT can significantly compromise the accuracy of these calculations.
• Identifying artifact-free phases is essential for reliable ventilation analysis.

Purpose of the Study:

• To investigate the similarity of ventilation distribution data derived from different respiratory phases in 4DCT.
• To assess the impact of motion artifacts on ventilation calculations.
• To determine optimal phases for accurate ventilation distribution analysis.

Main Methods:

• Analysis of 15 lung cancer cases using 4DCT.
• DIR performed between end-expiration and all other respiratory phases.
• Calculation of ventilation distributions using deformation matrices.
• Comparison of ventilation similarity using correlation coefficients (SCC) and Dice Similarity Coefficient (DSC).

Main Results:

• Reasonably good correlation between majority phases (average SCC 0.28-0.70 original, 0.30-0.75 smoothed).
• Higher correlation between neighboring phases (average SCC 0.55-0.70 original), indicating nonlinear dynamic ventilation.
• DSC analysis confirmed these trends.
• Artifact-free phases can reduce errors caused by motion.

Conclusions:

• Ventilation similarity across respiratory phases is quantifiable and shows nonlinear dynamics.
• Utilizing artifact-free phases can improve the accuracy of ventilation distribution calculations.
• Selecting phases closer to end-inspiration may minimize errors from dynamic ventilation nonlinearity.