Magnetic resonance imaging (MRI) of breast tissue is often used to definitively diagnose breast cancer due to its high soft-tissue contrast. However, its high cost and limited accessibility make it unsuitable for real-time screening, unlike ultrasound. Machine learning models that synthesize MRI from ultrasound could enable low-cost screening and triage by providing MRI-like tissue characterization during routine ultrasound exams. However, machine learning models trained to generate synthetic MRIs from ultrasound often struggle due to the absence of paired datasets across imaging modalities. This lack of paired datasets is a particular struggle in the field of model building for medical imaging purposes. We propose a physics-based method for generating synthetic ultrasound images from MRI volumes using mathematical modeling of acoustic beam propagation and tissue composition. We use a singular MRI example as a proof-of-concept of this physics simulation, which is based on pre-existing ultrasound physics. Each MRI slice was divided into a 2 × 2 grid to identify local beam entry regions. Tangent lines were calculated along each grid edge to estimate perpendicular ultrasound beam paths in a pinwheel fashion around the breast tissue. Initial simulations indicate that the generated ultrasound-like images reproduce plausible intensity gradients and textural variation consistent with known acoustic-tissue interactions. In resource-limited settings, access to MRI for breast cancer workup remains a significant barrier when ultrasound findings are inconclusive. To enable development of cross-modal synthesis models, we present a physics-based simulation framework that creates paired MRI-ultrasound training datasets by synthesizing realistic ultrasound images from available MRI data. This supports the feasibility of using physics-driven methods to generate synthetic ultrasound from MRI data. Because contrast enhancement and vascular permeability are not modeled, this framework should be interpreted as a non-contrast anatomical MRI to conventional B-mode ultrasound simulation rather than a complete cross-modal transformation that captures DCE-MRI or contrast-enhanced ultrasound behavior.
Muthukumar et al. (Thu,) studied this question.