Histology-based Microstructural Tissue Phantoms for Realistic Ultrasound Simulation

Ultrasound simulation has become an essential tool for transducer design, optimizing imaging strategies, and validating image analysis techniques. A simulation method that accommodates tissue-specific scattering would significantly improve realism of insilico phantoms, generating much needed training data with ground truth information (on anatomy, motion, function) available. This study presents a novel framework for constructing 2-D numerical tissue phantoms based on histological microstructure, enabling accurate and realistic ultrasound simulations. Whole-slide histology images of adipose fat, carotid artery, muscle, and skin were segmented to extract collagen and cellular components. Relative acoustic heterogeneity was estimated for all tissues, which was combined with the segmentations to generate spatial maps of density and speed of sound. Ultrasound simulations were performed using a pseudospectral wave solver and validated against ex vivo data. Quantitative analysis using the Jensen-Shannon Divergence and a multi-level texture anisotropy index demonstrated significantly improved realism in speckle patterns compared to baseline isotropic phantoms. The numerical phantoms combined with computed tomography-based patient geometries show promising results for realistic ultrasound dataset generation.