Andrew Kingston

OverviewAI-generated summary

Andrew Kingston's research focuses on advancing imaging techniques, particularly in the areas of X-ray computed tomography and ghost imaging. His work investigates methods for dose reduction in X-ray imaging through algorithmic optimization and the development of novel mask designs for patterned illumination. Kingston has explored the inherent dose-reduction potential of classical ghost imaging and optimized nonconfigurable, transversely displaced masks for illumination patterns.

His research also extends to the development of versatile microtomography systems for *in situ* studies of material properties, such as the failure and fragmentation in geomaterials. He has published on synchrotron-based X-ray fluorescence ghost imaging and the creation of universal masks for hard X-rays. More recently, Kingston has investigated the application of deep learning in classical X-ray ghost imaging for dose reduction and the use of synchrotron-based 3D X-ray computed tomography to reveal root system architecture in plants.

Kingston is an Associate Professor at the University of Arkansas at Little Rock. His scholarship metrics include an h-index of 22, 144 total publications, and 1,695 total citations. He has collaborated with Fumiya Watanabe, Janak Paudel, and John Nichols, all from the University of Arkansas at Little Rock.

Metrics

• h-index: 22
• Publications: 144
• Citations: 1,695