The role of harmonization in phantoms: a systematic analysis of various task-based scenarios

In medical imaging, harmonization plays a crucial role in reducing variability arising from diverse imaging devices and protocols. Patient images obtained by different scan conditions of computed tomography may show varying performance when processed by the same artificial intelligence model or quantitative metrics. The purpose of this study was to explore and analyze the role of harmonization within a virtual imaging trial platform. An evaluation framework consisting of three typical task-based scenarios was proposed: lung structure segmentation, chronic obstructive pulmonary disease (COPD) quantification, and lung nodule quantification. Evaluation results before and after harmonization reveal three findings: 1) 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 features with high intraclass correlation coefficient in lung nodule quantification. The results demonstrate the significant potential of harmonization across various task-based scenarios.