Development and CT image‐domain validation of a computational lung lesion model for use in virtual imaging trials

Purpose Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly‐customizable lesion detectability studies on clinical computed tomography (CT) data, the ground‐truth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomically‐informed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically‐informed model for use with simulated CT imaging studies. Methods The lesion model was simulated initially from a seed cell with 10 μm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete time‐intervals (corresponding to synchronous cell cycles) up to a size of ∼200 μm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time‐step given sufficient space. At lesion sizes beyond 200 μm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 μm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly‐varying material properties based on their cellular level health in each region, resulting in a multi‐material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images. Results The useable outputs of this work were voxel‐ or surface‐based, validated, computational lesions, at a scale clearly visible on clinical CT (3–4 cm). Analysis of the observer study results indicated that the computationally‐generated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non‐inferior to an earlier image‐based lesion model—indicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs. Conclusions Results indicated the non‐inferiority of this model as compared to previous methods, indicating the utility of the model for use in both hybrid CT images and in simulated CT imaging.