Stochastic Modeling of Normal and Tumor Tissue Microstructure for High-Frequency Ultrasound Imaging Simulations

High-frequency (20-60 MHz) ultrasound images of preclinical tumor models are sensitive to changes in tissue microstructure that accompany tumor progression and treatment responses, but the relationships between tumor microanatomy and high-frequency ultrasound backscattering are incompletely understood. This paper introduces a 3-D microanatomical model in which tissue is treated as a population of stochastically positioned spherical cells consisting of a spherical nucleus surrounded by homogeneous cytoplasm. The model is used to represent the microstructure of both healthy mouse liver and an experimental liver metastasis that are analyzed using 4´,6-diamidino-2-phenylindole- and hematoxylin and eosin-stained histology specimens digitized at 20times magnification. The spatial organization of cells is controlled in the model by a Gibbs-Markov point process whose parameters are tuned to maximize the similarity of experimental and simulated tissue microstructure, which is characterized using three descriptors of nuclear spatial arrangement adopted from materials science. The model can accurately reproduce the microstructure of the relatively homogeneous healthy liver and the average cell clustering observed in the experimental metastasis, but is less effective at reproducing the spatial heterogeneity of the experimental metastasis. The model provides a framework for computational investigations of the effects of individual microstructural and acoustic properties on high-frequency backscattering.