Chris Delnooz
Papers
1
Total Citations
2
H-Index
1
About
Chris Delnooz is a pioneering researcher in advanced computed tomography (CT) imaging, with a primary focus on multi-source array (MXA) systems for brain imaging. His major contribution lies in the development of a semi-stationary head CT prototype and its associated image formation algorithms, which address critical challenges in sampling and x-ray scatter inherent to multi-source CT architectures. Delnooz’s work introduces an adaptive scatter estimation method and leverages learned diffusion models for robust image reconstruction, enabling high-quality brain imaging with a stationary gantry. This innovation has the potential to reduce motion artifacts and improve patient accessibility in neuroimaging. His most-cited paper, published in 2024, has already garnered 2 citations, signaling early impact in the field. Delnooz’s achievements include the successful evaluation of his prototype system, demonstrating its feasibility for clinical translation. His research bridges hardware design and computational imaging, offering a transformative approach to CT that could reshape diagnostic workflows in neurology and emergency care.
Research Focus
Key Achievements
Top Papers
- 1