Ultrasound computed tomography (USCT) is an ultrasound imaging modality that relies on the transmission of ultrasonic energy through an object of interest. This is in contrast to conventional B-mode ultrasound that relies on reflected signals. USCT is able to produce quantitative images of the acoustic properties of tissue, such as the sound speed, density, and acoustic attenuation. USCT has a number of potential applications, including breast cancer screening. It has a number of advantages over existing imaging modalities. Compared with mammography, it is radiation-free, breast-compression-free, and relatively inexpensive. Compared with conventional B-mode ultrasound, it produces images with a large field-of-view whose quality is independent of the skill of the operator. It may also offer some advantages over mammography when screening women with dense breasts.
Most image reconstruction methods in USCT are based on approximate solutions to the acoustic wave equation, which ignore higher-order diffraction effects. This can result in reconstructed images with poor resolution. This problem can be overcome through the use of waveform-inversion-based image reconstruction methods that directly seek the solution of the acoustic wave equation. However, this approach is often computationally very expensive. Our work focuses on the development of efficient waveform inversion methods that accurately model the underlying physics of USCT imaging systems. We accomplish this through the use of optimization-based image reconstruction algorithms that leverage the latest work in stochastic optimization, machine learning, and computational acoustics. This project represents a collaboration with Dr. Neb Duric and his team.