Duke University Medical Physics Graduate Program

On April 10, 2026, the Duke Medical Physics Graduate Program hosted its Annual Research Symposium, bringing together students, faculty, and alumni for an exciting showcase of discovery and collaboration. The event featured 30 research posters highlighting cutting-edge work across the field.
We were especially honored to welcome Irina Vergalasova, PhD, DABR as keynote speaker and recipient of the 2025 Distinguished Alumni Award. Her leadership in brachytherapy physics, dedication to education, and lasting impact on the profession exemplify the very best of Duke Medical Physics.
The day concluded with recognition of outstanding student research—congratulations to all of our poster award winners for their exceptional work!
Read more: https://medicalphysics.duke.edu/news/medical-physics-graduate-program-annual-research-symposium-and-distinguished-alumni-awardee/

Great job and congratulations, Roberto Carrascosa, MS (Advisor: Scott Robertson, PhD) on defending your thesis on March 31, 2026, titled: “Development and Validation of Tools for Virtual Imaging Trials of Ultrafast Breast MRI.” Roberto Carrascosa’s research focused on development of software tools for simulating Ultrafast Breast MRI in order to optimize clinical protocols. This accomplishment reflects his hard work and dedication — well done, Roberto!

We are excited to share that Justin Simmons, MS (Advisor: Timothy Turkington, PhD) has successfully defended his Master’s thesis titled “Analysis of PET Image Quality Using 2D vs 3D ROIs in Small Spheres” on March 31, 2026. Congratulations to Justin on reaching this important academic milestone!

Please join us in celebrating Yakun Zhang, PhD (Advisor: Ehsan Samei, PhD), who successfully defended her PhD dissertation on March 27, 2026. Her dissertation, “Systemic Integration of Retrospective Analysis for Optimizing CT Imaging Practice,” focused on improving CT quality monitoring through quantitative image quality metrics that better reflect clinical performance. Specifically, she investigates organ-specific image noise and task-based detectability in large clinical CT datasets, examining how these measures vary with patient size, imaging technique, and reconstruction settings to identify clinically meaningful image quality differences across scanners and institutions.