Jie Deng, Ph.D.

Jie Deng, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center and faculty in the department’s Division of Medical Physics & Engineering. She is a certified magnetic resonance imaging (MRI) and MRI for radiation therapy medical physicist by the American Board of Medical Physics. She is also a magnetic resonance safety officer for the American Board of Medical Resonance Safety (ABMRS) as well as a member of UT Southwestern’s MRI Safety Committee.

Dr. Deng earned her bachelor’s degree in biomedical engineering from Southeast University in China and completed her master’s studies in bioengineering from University of Illinois at Chicago. She followed with the completion of her Ph.D. in biomedical engineering from Northwestern University in Chicago, where she also received her Master of Science in Law degree from the Northwestern Pritzker School of Law.

Dr. Deng has extensive clinical and research experience in MRI physics, quantitative MRI, oncological imaging, imaging processing, and artificial intelligence (AI) applications in medical imaging. Specifically, she has continuously worked on development, improvement, and clinical implementation of advanced MRI technologies for MRI-guided radiation therapy and imaging biomarkers for therapeutic response evaluation and biology-driven adaptive radiation therapy.

In the Department of Radiation Oncology, Dr. Deng is involved as both a mentor and lecturer for medical physics residents and graduate students on topics such as MR-only simulation procedures, MR-linear accelerator simulation practices, and medical imaging fundamentals.

Dr. Deng places a strong focus on advanced imaging and radiotherapy techniques, bridging both research and clinical applications. She actively participates in prestigious annual meetings, including the International Society for Magnetic Resonance in Medicine (ISMRM) and the American Association of Physics in Medicine (AAPM), where she delivers presentations on subjects that include MRI innovations, adaptive radiotherapy, and the integration of AI and deep learning in medical imaging.