Eze Ahanonu

Research Interests

Eze Ahanonu’s research focuses on quantitative MRI and deep learning for medical imaging. His work includes accelerated MRI acquisition, image reconstruction, and parameter mapping, with recent projects centered on abdominal T1 mapping, motion-robust imaging, and efficient quantitative imaging workflows.

He has also contributed to earlier work in image compression and machine learning, reflecting a broader interest in efficient and information-rich imaging methods.

Background

Eze Ahanonu is a PhD candidate in Electrical and Computer Engineering at the University of Arizona. He previously completed his undergraduate degree in Biomedical Engineering and his M.S. in Electrical and Computer Engineering at the University of Arizona.

His work sits at the intersection of engineering, computation, and medical imaging, with projects spanning MRI method development, deep learning, and translational imaging applications.

Highlights

His recent work has included oral presentations at ISMRM and research on deep learning-based abdominal MRI methods. He was selected for a 2021-22 ARCS Scholarship, received recognition for ISMRM presentations, and was also recognized in the University of Arizona’s 2025 patent celebration for collaborative work on a virtual reality training system for airway management.