Enhanced AI for Science using Diffusion-based Generative AI A Case Study on Ultrasound Computing Tomography

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods leverage accurate wave physics to generate high spatial resolution quantitative images of the breast tissue's acoustic properties, such as speed of sound, from USCT measurement data. However, the significant computational demand for FWI reconstruction poses a considerable challenge to its widespread adoption in clinical settings. Data-driven machine learning approaches offer a faster and more efficient means of translating waveform data into images. Yet, the effectiveness of machine learning methods is constrained by the diversity and quality of the training data. Given the heterogeneous distribution of breast tissue characteristics, such as fat content and size, the performance of machine learning varies across different sizes. This variability is problematic, particularly in medical diagnostics, where precision is crucial. In response to the limited data in certain categories, we propose utilizing generative AI to augment data samples, thereby enhancing FWI's performance on limited-sample data and addressing issues of AI fairness.