Using Parallel Computing to Enable Inexpensive, High Quality Ultrasonic Imaging

Ultrasound is a common imaging technique, often used in clinical settings. It is cheap and safe. However, it suffers from lower quality than other techniques, such as MRI and CAT. This project is exploring a novel approach that combines high-bandwidth ultrasound technologies with efficient algorithms that can create high-quality images.

A key part of this approach is the use of parallel computing to make the computations fast enough for real or near-real time imaging. We will start with inverse scattering algorithms developed by Prof. Weng Chew, one of the co-PIs of the project, analyze them for performance and scalability, and design and implement highly efficient versions of those algorithms. We will work with Prof. Chew and his student on the development and testing of the algorithms. Prof. Michael L. Oelze is also part of the project and will provide imaging data and expertise on the using ultrasound data for imaging.

I am looking for one graduate student interested in a research assistantship to work on this project, starting immediately.

Contact: Bill Gropp