Synthesizing heterogeneous lung lesions for virtual imaging trials

Virtual imaging trials of malignancies require realistic models of lesions. The purpose of this study was to create hybrid lesion models and associated tool incorporating morphological and textural realism. The developed tool creates a lesion morphology based on input parameters describing its shape and spiculation. Internal heterogeneity is added as 3D clustered lumpy background (CLB), allowing for various sub-classes of lesions including full solid, semi-solid, and ground-glass lesions. To insert a lesion into a full body human model (e.g., XCAT phantom), the edges of the lesion are blended into the surrounding background using a parameterizable Gaussian blurring technique. The developed lesion tool allows users to define lesion sizes either manually or automatically following population distribution of lesion sizes. Similarly, the tool allows users to insert lesions either manually or automatically while avoiding intersections with pulmonary structures. The utility of the developed lesion tool was demonstrated by modeling both homogeneous and heterogeneous lung lesions and inserting them into 5 human models (XCAT). The human models were imaged using a validated CT simulator (DukeSim). Images of heterogeneous lesions were visually comparable to clinical images. The first order and texture radiomics features (58 features) were extracted from all image series and compared using the Pearson correlation. The two lesion generation techniques for full solid lesions (homogeneous vs. heterogeneous) were observed to have a weak correlation (r<0.4) for 35 of 58 features using a soft kernel, and for 43 of 58 features using a sharp kernel—capturing the structural differences between the two models. The lesion tool proved capable of forming different lung lesion sub-classes (full-solid, semi-solid, and ground-glass) through its input parameters to emulate the lesion characteristics of interest for a virtual lesion study.