Abstract
In medical imaging, harmonization plays a crucial role in reducing variability arising from diverse imaging devices and protocols. Patient images obtained under different computed tomography (CT) scan conditions may show varying performance when analyzed using an artificial intelligence model or quantitative assessment. This necessitates the need for harmonization. Virtual imaging trial (VIT) through digital simulation can be used to develop and assess the effectiveness of harmonization models to minimize data variability. The purpose of this study was to assess the utility of a VIT platform for harmonization across a range of lung imaging scenarios. To ensure consistent and reliable analysis across different virtual imaging datasets, we conducted a multi-objective assessment encompassing three typical task-based scenarios: lung structure segmentation, chronic obstructive pulmonary disease (COPD) quantification, and lung nodule quantification. A physics-informed deep neural network was applied as the unified harmonization model for all three tasks. Evaluation results before and after harmonization reveal three findings: 1) modestly improved Dice scores and reduced Hausdorff Distances at 95th Percentile in lung structure segmentation; 2) decreased variation in biomarkers and radiomics features in COPD quantification; and 3) increased number of radiomics features with high intraclass correlation coefficient in lung nodule quantification. The results demonstrate the significant potential of harmonization across various task-based scenarios and provide a benchmark for the design of efficient harmonizers.