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Pulmonary emphysema is a form of Chronic Obstructive Pulmonary Disease (COPD) and a chronic lung condition that results in a breakdown of alveoli walls. Quantitative Computed Tomography (CT) is increasingly used to assess the presence or progression of emphysema. CT quantifications are affected by the acquisition protocols and scanner makes and models. This variability is a major concern for cross-sectional and longitudinal disease characterizations with largescale, multi-institutional datasets. Therefore, CT images need to be harmonized to reflect the patient condition and not the attributes of the imaging systems. The purpose of this study was to develop a physics-based harmonization framework that transforms CT images to a reference quality index (iso resolution and noise conditions) enabling robust emphysema quantifications across varied CT conditions. The harmonizer was developed using a virtual imaging trial (VIT) platform taking advantage of ground truth knowledge and control over CT parameters in VIT. The developed harmonizer was applied to clinical data from the COPDGene dataset to demonstrate its clinical utility. The established imaging biomarkers of “LAA-950” and “Perc15” were selected for emphysema quantifications. Results demonstrated that the harmonizer improved the quantification performance by reducing the bias in “LAA-950" from 7.03 (𝐶𝐼: [6.38, 7.68]) to 0.14 (𝐶𝐼: [0.08, 0.20]) after matching for kernel and from 2.48 (𝐶𝐼: [2.21, 2.76]) to −0.34 (𝐶𝐼: [−0.48, −0.20]) after matching for noise settings on the COPDGene dataset. This developed harmonization framework provides robust emphysema quantifications, enabling objective disease characterizations in large-scale, multi-center, and longitudinal studies.
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