Data-Driven Synthesis of Spiculated Breast Phantoms: A User-Interactive Framework for Modeling and Clinic Image Integration

Abstract

The advancement of virtual clinical trials (VCT) and artificial intelligence (AI) systems in breast imaging requires large, diverse datasets with reliable annotations. However, the limited availability of clinical datasets and the uncontrollable variability of biological lesion characteristics create a critical bottleneck. While computational phantoms offer an alternative, current methodologies often lack clinically realistic morphological irregularity and fail to provide a physics-aware image fusion technique, resulting in unnatural artifacts when inserted into clinical host images.This thesis proposes a novel, multi-stage interactive software framework for the procedural generation of 3D spiculated breast mass phantoms and their physics-aware insertion into full-field digital mammograms (FFDM). The framework utilizes clinical FFDMs to extract a 2D mass contour, generating a 3D core via a modulated extrusion technique adapted by local intensity and parabolic shape factors. Complex spiculation patterns are simulated using an iterative fractal branching algorithm analogous to a stochastic Lindenmayer system (L-system). Finally, the 2D projection of the synthesized phantom is integrated into clinical images using a physics-based additive superposition model with a targeted brightness calibration factor, ensuring rigorous contrast preservation across different breast tissue densities. Reader studies and quantitative radiomics analysis (evaluating circularity, solidity, and Weber Contrast) demonstrated that the generated phantoms achieve moderate-to-high visual realism. The physics-based insertion algorithm successfully calculated realistic attenuation levels regardless of background parenchymal complexity. While the framework effectively replicates benign lesion features, future refinement through radiomics-guided modeling is needed to better capture the highly chaotic morphology of high-grade malignancies. Ultimately, this tool provides a practical, cost-effective solution for systematically evaluating breast imaging technologies.