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. 2018 Sep:11040:180-190.
doi: 10.1007/978-3-030-00946-5_19. Epub 2018 Sep 12.

A CT Scan Harmonization Technique to Detect Emphysema and Small Airway Diseases

Affiliations

A CT Scan Harmonization Technique to Detect Emphysema and Small Airway Diseases

Gonzalo Vegas-Sánchez-Ferrero et al. Image Anal Mov Organ Breast Thorac Images (2018). 2018 Sep.

Abstract

Recent studies have suggested the central role of small airway destruction in the pathogenesis of COPD leading to further parenchymal destruction. This evidence has sparked the interest in in-vivo assessment of small airway disease overall at the early onset of the disease. The parametric response mapping (PRM) technique has been proposed to distinguish gas trapping due to small airway disease from low attenuation areas due to emphysema. Despite its success, the PRM technique shows some limitations that are precluding the interpretation of its results. The density value used to assess gas trapping highly depends on acquisition parameters, such as dose and reconstruction kernel, and changes in body size, that introduce inhomogeneous photon absorption patterns. In particular, many studies using PRM employ inspiratory and expiratory images that are obtained at different dose levels. Emphysema impact in early disease may be confounded with the gas trapping due to the noise introduced by differences in the acquisition during the PRM. In this work, we propose a CT harmonization technique to remove the nuisance factors to distinguish between small airway disease and emphysema. Our results show that the measurements based on CT harmonization provide an increase in the detection of both emphysema and airway disease, resulting in a statistically significant impact of both components and a better association with lung function measures.

Keywords: CT scans; Emphysema; Lung disease; Statistical characterization.

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Figures

Fig. 1.
Fig. 1.
Inspiratory and expiratory scans used for PRM analysis. Note that the noise variance remarkably increases in the expiratory scan due to the lower dose applied. Additionally, the noise changes across the image affecting the PRM analysis.
Fig. 2.
Fig. 2.
Linear regression between local mean and local variance in the trachea before and after calibration.
Fig. 3.
Fig. 3.
Left: Distribution of Emph% (a) and FSAD% (b) in the analyzed population across filtering methods. The proposed harmonization approach increased the ability to resolve more emphysema and small airway disease. Right: PRM analysis for the inspiratory scans; original (c) and harmonized (d).

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