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

TL;DR

This study presents a two-stage pipeline combining kernel harmonization via cycle GANs and deformable registration to improve the analysis of paired inspiratory-expiratory chest CT scans in COPD patients, reducing measurement inconsistencies.

Contribution

It introduces a novel approach to harmonize reconstruction kernels and enhance deformable registration accuracy in lung CT analysis for COPD.

Findings

Harmonization reduces emphysema measurement variability.

Registration accuracy improves significantly after harmonization.

Deformable registration remains robust despite kernel differences.

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.