Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software

Rationale and Objectives: A standard lung template could improve population-level analyses for computed tomography (CT) scans of the lung. We develop a fully automated preprocessing pipeline for image analysis of the lungs using updated methodologies and R software that results in the creation of a standard lung template. We apply this pipeline to CT scans from a sarcoidosis population, exploring the influence of registration on radiomic analyses. Materials and Methods: Using 65 high-resolution CT scans from healthy adults, we create a standard lung template by segmenting the left and right lungs, nonlinearly registering lung masks to an initial template mask, and using an unbiased, iterative procedure to converge to a standard lung shape (Dice similarity coefficient ≥0.99). We compare three-dimensional radiomic features between control and sarcoidosis patients, before and after registration to a study-specific lung template. Results: The final lung template had a right lung volume of 2967 cm³ and left lung volume of 2623 cm³, with a median HU = −862. Registration significantly affected radiomic features, shifting the HU distribution to the left, decreasing variability, and increasing smoothness (p < 0.0001). The registration improved detective ability of radiomics; for contrast, autocorrelation, energy, and homogeneity, the group effect was significant postregistration (p < 0.05), but was not significant preregistration. Conclusion: The final lung template and software used for its creation are publicly available via the lungct R package to facilitate its use in practice. This study advances lung imaging by developing tools to improve population-level analyses for various lung diseases.