Ti Bai, Ph.D.

Ti Bai, Ph.D., part of the Department of Radiation Oncology’s Division of Medical Physics & Engineering, is a scientist with expertise in low-dose X-ray CT imaging, medical image analysis, and deep learning algorithms. He completed his Ph.D. in 2017 at the Xi’an Jiaotong University, where he majored in low-dose CT reconstruction. After graduation, he worked for the Institute of Deep Learning (IDL) at Baidu Research in Beijing for two years.

Dr. Bai joined the Medical Artificial Intelligence and Automation (MAIA) Laboratory at UT Southwestern in 2019 as a postdoctoral researcher, and named as an instructor in 2021. After joining MAIA lab, his focus shifted to designing, developing, and translating innovative and practical artificial intelligence technologies to improve health care performance and efficiency.

• Artificial intelligence
• Medical image analysis
• Medical imaging

Featured Publications

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography.

Bai T, Wang B, Nguyen D, Wang B, Dong B, Cong W, Kalra MK, Jiang S, IEEE Trans Med Imaging 2021 Jul PP

Probabilistic self-learning framework for low-dose CT denoising.

Bai T, Wang B, Nguyen D, Jiang S, Med Phys 2021 May 48 5 2258-2270

Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning.

Li W, Kazemifar S, Bai T, Nguyen D, Weng Y, Li Y, Xia J, Xiong J, Xie Y, Owrangi AM, Jiang SB, Biomed Phys Eng Express 2021 Feb

A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision

Ti Bai, Anjali Balagopal, Michael Dohopolski, Howard E. Morgan, Rafe McBeth, Jun Tan, Mu-Han Lin, David J. Sher, Dan Nguyen, Steve Jiang arXiv 2021

Deep Dose Plugin: Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm

Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang Machine Learning: Science and Technology 2021

Deep High-Resolution Network for Low Dose X-ray CT Denoising

Ti Bai, Dan Nguyen, Biling Wang and Steve Jiang Journal of Artificial Intelligence for Medical Sciences 2021

A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction

Jianhui Ma, Dan Nguyen, Ti Bai, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve Jiang Medical Physics 2021

Z-Index Parameterization (ZIP) for Volumetric CT Image Reconstruction via 3D Dictionary Learning

Ti Bai, Hao Yan, Xun Jia, Steve B. Jiang, Ge Wang and Xuanqin Mou IEEE Transactions on Medical Imaging 2017

Data correlation based noise level estimation for cone beam projection data

Ti Bai, Hao Yan, Luo Ouyang, David Staub, Jing Wang, Xun Jia, Steve B. Jiang, and Xuanqin Mou Journal of X-ray Science and Technology 2017

A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy

Xu Yuan, Bai Ti, Yan Hao, Ouyang Luo, Pompos A, Wang Jing, Zhou Linghong, Jiang Steve, Jia Xun Physics in Medicine and Biology 2015

• American Association of Physicists in Medicine (2020)