Medical Engineering Distinguished Seminar Series, Ge Wang

Computed tomography (CT) has evolved through several methodological paradigms, from analytic reconstruction to iterative optimization and, more recently, AI-based imaging. This talk will review this progression. I will first revisit analytic CT reconstruction, highlighting cone-beam spiral CT, and then discuss iterative reconstruction, using interior tomography as an example. I will next examine the emerging AI paradigm, with emphasis on deep reconstruction, foundation models, and uncertainty-aware methods. Rather than viewing these approaches as competing alternatives, I will discuss how future CT systems may increasingly integrate their complementary strengths, offering a path toward lower-dose, more accurate, and ultimately more preventive CT imaging. The talk will conclude with our recent explorations of AI-enabled journal management and graduate education in medical imaging.

Biography: Ge Wang is the Clark & Crossan Endowed Chair Professor and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute. His research spans spiral cone-beam CT, interior tomography, low-dose CT, photon-counting CT, and AI–based tomographic imaging, with impact in both academia and industry. He serves as the Editor-in-Chief of IEEE Transactions on Medical Imaging. Dr. Wang is a Clarivate Highly Cited Researcher and a Fellow of IEEE, SPIE, Optica, AAPM, AIMBE, AAAS, and NAI. His selected honors include the AAPM Edith H. Quimby Lifetime Achievement Award, IEEE Engineering in Medicine and Biology Society Career Achievement Award, SPIE Aden and Marjorie Meinel Technology Achievement Award, Sigma Xi Walston Chubb Award for Innovation, and IEEE Region 1 Outstanding Teaching Award. https://www.linkedin.com/in/ge-wang-axis

Hosted by Professor Lihong Wang