Qing Lyu | Engineering | Best Researcher Award

Dr. Qing Lyu | Engineering | Best Researcher Award

Wake Forest University School of Medicine | United States

Dr. Qing Lyu is a highly accomplished researcher in biomedical engineering and AI-driven medical imaging, with a strong track record of innovation and scholarly impact. He holds a B.S. and M.S. in Biomedical Engineering from Shanghai Jiao Tong University and a Ph.D. from Rensselaer Polytechnic Institute, where his dissertation focused on deep neural networks for MRI applications. Currently, he serves as Assistant Professor in Radiology and Adjunct Assistant Professor in Biomedical Engineering at Wake Forest University School of Medicine. Dr. Qing Lyu’s research spans MRI and CT super-resolution, multimodal radiomics, deep learning for disease prediction, and AI-based clinical translation. His scholarly output includes 14 documents indexed in Scopus, accruing 594 citations and an h-index of 8, reflecting both quality and influence. He holds multiple patents, has secured competitive grants, and serves on editorial boards while reviewing for top journals and conferences, underscoring his leadership in advancing biomedical imaging, AI, and translational medical research.

Profile: Scopus | Google Scholar

Featured Publications

Lyu, Q., Tan, J., Zapadka, M. E., Ponnatapura, J., Niu, C., Myers, K. J., Wang, G., … & Whitlow, C. (2023). Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: Results, limitations, and potential. Visual Computing for Industry, Biomedicine, and Art, 6(1), 9.

Lyu, Q., Shan, H., Steber, C., Helis, C., Whitlow, C., Chan, M., & Wang, G. (2020). Multi-contrast super-resolution MRI through a progressive network. IEEE Transactions on Medical Imaging, 39(9), 2738–2749.

Lyu, Q., Shan, H., & Wang, G. (2020). MRI super-resolution with ensemble learning and complementary priors. IEEE Transactions on Computational Imaging, 6, 615–624.

Lyu, Q., & Wang, G. (2022). Conversion between CT and MRI images using diffusion and score-matching models. arXiv preprint arXiv:2209.12104.

Niu, C., Li, M., Fan, F., Wu, W., Guo, X., Lyu, Q., & Wang, G. (2022). Noise suppression with similarity-based self-supervised deep learning. IEEE Transactions on Medical Imaging, 42(6), 1590–1602.