Investigating the impact of kernel harmonization and deformable registration on inspiratory and expiratory chest CT images for people with COPD

2025-02-07

AI Summary

This study addresses reconstruction kernel-induced bias in emphysema quantification during inspiratory–expiratory CT registration in COPD patients, arising from mismatched kernels (e.g., BONE vs. STANDARD). We propose a two-stage “kernel harmonization + deformable registration” framework: first, CycleGAN is employed to translate hard-kernel inspiratory images into soft-kernel style; second, voxel-based deformable registration is performed on harmonized images. To our knowledge, this is the first work jointly modeling kernel harmonization and registration to enhance quantitative robustness. Evaluated on the COPDGene dataset, kernel harmonization reduced the median emphysema fraction from 10.479% to 3.039%, approaching the reference soft-kernel value (1.305%). Subsequent registration significantly improved Dice similarity coefficients of emphysema masks (p < 0.001), demonstrating effective suppression of kernel-related artifacts and enhanced accuracy in regional volume change analysis.

Technology Category

• Machine Learning: Kernel Methods
• Computer Vision: Segmentation
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

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Abstract

Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deformable image registration of these scans assesses regional lung volumetric changes. However, variations in reconstruction kernels between paired scans introduce errors in quantitative analysis. This work proposes a two-stage pipeline to harmonize reconstruction kernels and perform deformable image registration using data acquired from the COPDGene study. We use a cycle generative adversarial network (GAN) to harmonize inspiratory scans reconstructed with a hard kernel (BONE) to match expiratory scans reconstructed with a soft kernel (STANDARD). We then deformably register the expiratory scans to inspiratory scans. We validate harmonization by measuring emphysema using a publicly available segmentation algorithm before and after harmonization. Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10.479% to 3.039%, with a reference median score of 1.305% from the STANDARD kernel as the target. Registration accuracy is evaluated via Dice overlap between emphysema regions on inspiratory, expiratory, and deformed images. The Dice coefficient between inspiratory emphysema masks and deformably registered emphysema masks increases significantly across registration stages (p<0.001). Additionally, we demonstrate that deformable registration is robust to kernel variations.

Problem

• Harmonize CT reconstruction kernels
• Perform deformable image registration
• Quantify emphysema in COPD patients

Innovation

• Cycle GAN for kernel harmonization
• Deformable image registration
• Reduces emphysema measurement inconsistencies