Xi Chen, Chaoyang Zhang, Ti Bai, Shaojie Chang, “Improving Spectral CT Image Quality Based on Channel Correlation and Self-Supervised Learning”, IEEE Transactions on Computational Imaging, 2023, paper
Zhi, Shaohua, Yinghui Wang, Haonan Xiao, Ti Bai, Hong Ge, Bing Li, Chenyang Liu, Wen Li, Tian Li, and Jing Cai, “Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution.” IEEE Transactions on Medical Imaging, 2023, paper
Xiao Liang, Jaehee Chun, Howard Morgan, Ti Bai, Dan Nguyen, Justin C. Park, Steve Jiang, “Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy”, Medical Physics, 2023, paper
Anjali Balagopal, Michael Dohopolski, Young Suk Kwon, Steven Montalvo, Howard Morgan, Ti Bai, Dan Nguyen, Xiao Liang, Xinran Zhong, Mu-Han Lin, Neil Desai, Steve Jiang, ”Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer.” arXiv, 2023, paper
Xiao Liang, Howard Morgan, Ti Bai, Michael Dohopolski, Dan Nguyen, Steve Jiang, “Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy.” Physics in Medicine & Biology, 2023, paper
Shaojie Chang, Yongfeng Gao, Marc J. Pomeroy, Ti Bai, Hao Zhang, Siming Lu, Perry J. Pickhardt, Amit Gupta, Michael J. Reiter, Elaine S. Gould, and Zhengrong Liang, “Exploring dual-energy CT spectral information for machine learning-driven lesion diagnosis in pre-log domain”, IEEE Transactions on Medical Imaging, 2023 paper
Anjali Balagopal, Dan Nguyen, Ti Bai, Michael Dohopolski, Mu-Han Lin, Steve Jiang, “Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation”, arXiv, 2022, paper
Wufeng Xue, Heng Cao, Junqiang Ma, Ti Bai, Tianfu Wang, Dong Ni, “Improved Segmentation of Echocardiography With Orientation-Congruency of Optical Flow and Motion-Enhanced Segmentation”, IEEE Journal of Biomedical and Health Informatics, 2022, paper
Biling Wang, Michael Dohopolski, Ti Bai, Junjie Wu, Raquibul Hannan, Neil Desai, Aurelie Garant, Dan Nguyen, Xinlei Wang, Mu-Han Lin, Robert Timmerman, Steve Jiang, “Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy”, arXiv, 2022, paper
Michael Dohopolski, Kai Wang, Biling Wang, Ti Bai, Dan Nguyen, David Sher, Steve Jiang, Jing Wang, “Uncertainty estimations methods for a deep learning model to aid in clinical decision-making – a clinician's perspective”, arXiv, 2022, paper
Ti Bai, Anjali Balagopal, Michael Dohopolski, Howard E. Morgan, Rafe McBeth, Jun Tan, Mu-Han Lin, David J. Sher, Dan Nguyen, Steve Jiang, “A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Editing”, Radiology: AI, 2022, paper
Yongfeng Gao, Ti Bai, Siming Lu, Shaojie Chang, Hao Zhang, Mahsa Hoshmand-Kochi, Zhengrong Liang, “Markov random field texture generation with an internalized database using a conditional encoder-decoder structure”, SPIE, 2022, paper
Ti Bai, Muhan Lin, Xiao Liang, Biling Wang, Michael Dohopolski, Bin Cai, Dan Nguyen, Steve Jiang, “Region Specific Optimization (RSO)-based Deep Interactive Registration”, arXiv, 2022, paper
Chaoyang Zhang, Shaojie Chang, Ti Bai, Xi Chen, “S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement”, CT Meeting, 2022, paper
Hua-Chieh Shao, Jing Wang, Ti Bai, Jaehee Chun, Justin C. Park, Steve B. Jiang and You Zhang, “Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling”, Physics in Medicine & Biology, 2022, paper
Ti Bai, Biling Wang, Dan Nguyen, Bao Wang, Bin Dong, Wenxiang Cong, Mannudeep K. Kalra, Steve Jiang, “Deep Interactive Denoiser (DID) for X-Ray Computed Tomography”, IEEE Transactions on Medical Imaging, 2021, paper
Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang, “Deep Dose Plugin: Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm”, Machine Learning: Science and Technology, 2021, paper
Ti Bai, Biling Wang, Dan Nguyen, Steve Jiang, “Probabilistic Self-learning Framework for Low-dose CT Denoising”, Medical Physics, 2021, paper
Ti Bai, Dan Nguyen, Biling Wang and Steve Jiang, “Deep High-Resolution Network for Low Dose X-ray CT Denoising”, Journal of Artificial Intelligence for Medical Sciences, 2021, paper
Wen Li, Ti Bai, Samaneh Kazemifar, Dan Nguyen, Yaochung Weng, Yafen Li, Jun Xia, Jing Xiong, Yaoqin Xie, Amir Owrangi, Steve Jiang, “Synthesizing CT Images from MR Images with Deep Learning: Model Generalization for Different Datasets through Transfer Learning”, Biomedical Physics & Engineering Express, 2021, paper
Jianhui Ma, Dan Nguyen, Ti Bai, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve Jiang, “A Feasibility Study on Deep Learning Based Individualized 3D Dose Distribution Prediction”, Medical Physics, 2021, paper
Jianhui Ma, Ti Bai, Dan Nguyen, Michael Folkerts, Xun Jia, Weiguo Lu, Linghong Zhou, Steve Jiang, “Individualized 3D Dose Distribution Prediction Using Deep Learning”, MICCAI, 2019
Gao, Yuan, Xingyuan Bu, Yang Hu, Hui Shen, Ti Bai, Xubin Li, and Shilei Wen. “Solution for large-scale hierarchical object detection datasets with incomplete annotation and data imbalance.”, arXiv:1810.06208, 2018, paper
Ti Bai, Hao Yan, Xun Jia, Steve B. Jiang, Ge Wang and Xuanqin Mou, “Z-Index Parameterization (ZIP) for Volumetric CT Image Reconstruction via 3D Dictionary Learning”, IEEE Transactions on Medical Imaging, 2017, paper
Ti Bai, Hao Yan, Luo Ouyang, David Staub, Jing Wang, Xun Jia, Steve B. Jiang, and Xuanqin Mou,“Data correlation based noise level estimation for cone beam projection data”, Journal of X-ray Science and Technology, 2017, paper
Ti Bai, Xuanqin Mou, Hao Yan, Hengyong Yu and Ge Wang, “A unified x-ray computed tomographic reconstruction framework”, CT Meeting, 2016
Xu Yuan, Bai Ti, Yan Hao, Ouyang Luo, Pompos A, Wang Jing, Zhou Linghong, Jiang Steve, Jia Xun, “A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy.” Physics in Medicine and Biology, 2015, paper
Chang S, Zhang Y, Ti Bai, et al, “A simple but effective denoising algorithm in projection domain of CBCT”, ICIG, 2015, paper
Ti Bai, Xuanqin Mou, Wufeng Xue, Hao Yan and Steve B. Jiang, “Iterative CT Reconstruction with Regularization Parameter Tuned by Blind Image Quality Assessment”, Fully3D, 2015
Xuanqin Mou, Ti Bai, Xi Chen, Hengyong Yu, Qingsong Yang and Ge Wang, “Optimal Selection for Regularization Parameter in Iterative CT Reconstruction Based on the Property of Natural Image Statistics”, Fully3D, 2015
Yan, H.; Wang, X.; Shi, F.; Ti Bai; Folkerts, M.; Cervino, L.; Jiang, S. B.; Jia, X., “Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: Cone/ring artifact correction and multiple GPU implementation”, Medical Physics, 2014, paper
Mou, X.; Wu, J.; Ti Bai; Xu, Q.; Yu, H.; Wang, G., “Dictionary learning based low-dose x-ray CT reconstruction using a balancing principle”, SPIE Medical Imaging, 2014
Ti Bai, Xuanqin Mou, Qiong Xu and Yanbo Zhang, “Noise Energy Estimation Based on the Sinogram and its Application to the Regularization Parameter Selection for Statistical Iterative Reconstruction”, Fully3D, 2013
Ti Bai, Xuanqin Mou, Qiong Xu, Yanbo Zhang, “Low-dose CT reconstruction based on multiscale dictionary”, SPIE Medical Imaging, 2013.