Multimodality in silico imaging of dense breasts: a comparative study of ultrasound computed tomography and digital breast tomosynthesis

This study aimed to develop a software tool to simulate ultrasound computed tomography (USCT) scans of a realistic anthropomorphic breast phantom. The tool was incorporated in an existing simulation framework for x-ray based imaging, resulting in a multimodality in silico evaluation framework. Digital breast models were developed utilizing advanced mathematical modeling based on procedural generation techniques to closely mimic the complex structural and compositional heterogeneity observed in human breast tissue. The breast's acoustic properties, including the speed of sound, density, and acoustic attenuation, were stochastically incorporated into the phantom in preparation for USCT simulation. Full waveform inversion was employed to reconstruct the ultrasound images. We were able to image a dense breast model with multiple modalities in silico. In addition to USCT simulation with the newly developed software, digital breast tomosynthesis, mammography, and computed tomography images of the same phantom were simulated using the GPU-accelerated Monte Carlo transport software MC-GPU. The presented in silico methods can be used to compare the performance of imaging technologies based on completely different physical characteristics, such as x-ray attenuation or ultrasound speed of sound. In silico evaluation has the advantage of enabling task-based evaluation in a fixed anatomy with known disease ground-truth, and without exposure of patients to ionizing radiation. With the required level of verification and validation, this framework could enhance our understanding of the performance of USCT as a standalone modality or as an adjunct to x-ray modalities, potentially providing in silico evidence that could be leveraged in regulatory evaluation of new USCT devices.