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Quantitative computed tomography (CT) plays a major role in large-scale, longitudinal, multi-center clinical trials. Minimizing measurement variation by identifying robust CT imaging biomarkers and developing robust techniques for quantitative CT has implications for clinical trial management and for patient care. We investigated robustness with respect to two sources of measurement variation in quantitative CT: repeat-scan variation (reproducibility) and variation due to changing CT technical parameters. In this dissertation, we conducted two separate but related studies in the area of quantitative CT robustness. In the first, we characterized and compared the reproducibilities of several widely-accepted measures of emphysema by examining repeat CT images from a multi-center clinical trial taken one week apart. We investigated the influence of breathhold on reproducibility of emphysema measures. We also investigated variations in reproducibility characteristics across sites. Our results have implications for multi-center clinical trials that rely on accurate and reproducible measures of emphysema. In the second study, we investigated feature and classifier robustness with respect to slice thickness, reconstruction kernel, and tube current in the setting of classification of fibrotic interstitial lung disease (FILD). We developed a quantitative Robustness Index measure by examining the stability of imaging features across multiple systematic reconstructions of CT raw sinogram data. We proposed a novel Robustness-Driven Feature Selection (RDFS) method for identifying a subset of robust features, then used these features to develop a robust support vector classifier for lung structure and parenchymal abnormalities in FILD. We demonstrated the superior robustness of this classifier compared to a similar classifier that did not utilize RDFS. Our results have implications for improving the robustness of classifier-based CT CAD systems, which is of importance in multi-center clinical trials that rely on imaging biomarkers that can be generalized across many sites and timepoints. |
