Abstract
PURPOSE: Differentiating between benign and malignant masses is one of the biggest challenges in breast imaging. The challenge is ingrained in the similarity of the attenuation coefficients between different types of lesion tissues and fibroglandular tissues. Contrast-enhanced imaging techniques can take advantage of the differing metabolism in different tissues, therefore, potentially allowing better differentiation of malignant and benign lesions. To facilitate the development and optimization of such technologies, we propose a fully digital 4D phantom that features time-varying enhancement patterns for different tissue types. APPROACH: The 4D model is based on a static, anthropomorphic 3D digital breast phantom. Masses inserted into the 3D phantom are based on a previously published model. Physiological parameters that capture the key characteristics of masses, e.g., wash-in and wash-out rates indicating metabolic level, are employed in the model to simulate fundamental features for categorizing mass types. The two-compartmental model, a well-known model in the field of pharmacokinetics, is used to depict the diffusion process of the contrast agent. Two methods are proposed to allow for the simulations of lesions with necrotic cores of varying shapes and sizes. RESULTS: The fourth dimension of the phantom models different time-varying enhancement patterns for different materials including fibroglandular tissue and lesion tissue. Metabolic characteristics of mass models can be adjusted to provide different enhancement patterns. The parameters of the 4D phantom can also be adjusted to fit different scenarios. The usage of the phantom is demonstrated by simulating mammograms at different time frames. CONCLUSION: A 4D digital anthropomorphic breast phantom that models different time-varying contrast enhancement patterns is presented. This phantom could be an integral tool for use in in silico trials to assess image quality of iodinated contrast-enhanced mammography, digital breast tomosynthesis, and breast computed tomography systems.