Ti Bai

540 total citations
37 papers, 345 citations indexed

About

Ti Bai is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Radiation. According to data from OpenAlex, Ti Bai has authored 37 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Biomedical Engineering and 13 papers in Radiation. Recurrent topics in Ti Bai's work include Medical Imaging Techniques and Applications (26 papers), Advanced X-ray and CT Imaging (16 papers) and Advanced Radiotherapy Techniques (12 papers). Ti Bai is often cited by papers focused on Medical Imaging Techniques and Applications (26 papers), Advanced X-ray and CT Imaging (16 papers) and Advanced Radiotherapy Techniques (12 papers). Ti Bai collaborates with scholars based in United States, China and Hong Kong. Ti Bai's co-authors include Steve Jiang, Hao Yan, Xun Jia, Jing Wang, Dan Nguyen, Luo Ouyang, Yuan Xu, Linghong Zhou, Arnold Pompoš and Xuanqin Mou and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, IEEE Transactions on Medical Imaging and Physics in Medicine and Biology.

In The Last Decade

Ti Bai

35 papers receiving 343 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ti Bai United States 9 299 195 164 55 41 37 345
Tsuicheng Chiu United States 9 172 0.6× 135 0.7× 106 0.6× 49 0.9× 70 1.7× 29 317
Raymond Schulz United States 8 190 0.6× 148 0.8× 130 0.8× 82 1.5× 29 0.7× 21 325
Alexander Grimwood United Kingdom 6 201 0.7× 135 0.7× 183 1.1× 122 2.2× 24 0.6× 11 320
Hugo Furtado Austria 12 244 0.8× 119 0.6× 236 1.4× 139 2.5× 75 1.8× 27 378
J. Wyatt United Kingdom 8 306 1.0× 123 0.6× 274 1.7× 76 1.4× 22 0.5× 25 367
Supriyanto Ardjo Pawiro Indonesia 9 238 0.8× 101 0.5× 176 1.1× 119 2.2× 90 2.2× 64 343
Dao Lam United States 7 247 0.8× 122 0.6× 213 1.3× 114 2.1× 33 0.8× 9 339
Joscha Maier Germany 16 623 2.1× 557 2.9× 147 0.9× 101 1.8× 28 0.7× 61 730
Jiawei Fan China 11 402 1.3× 133 0.7× 439 2.7× 199 3.6× 41 1.0× 29 553

Countries citing papers authored by Ti Bai

Since Specialization
Citations

This map shows the geographic impact of Ti Bai's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ti Bai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ti Bai more than expected).

Fields of papers citing papers by Ti Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ti Bai. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ti Bai. The network helps show where Ti Bai may publish in the future.

Co-authorship network of co-authors of Ti Bai

This figure shows the co-authorship network connecting the top 25 collaborators of Ti Bai. A scholar is included among the top collaborators of Ti Bai based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ti Bai. Ti Bai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dohopolski, Michael, et al.. (2025). Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer. Machine Learning Science and Technology. 6(1). 15015–15015. 1 indexed citations
2.
Bai, Ti, et al.. (2025). Cone Beam Computed Tomography-Guided Online Adaptive Radiation Therapy: Clinical Insights From a Nationwide Staffing Survey. International Journal of Radiation Oncology*Biology*Physics. 122(4). 884–892. 1 indexed citations
3.
Bai, Ti, Dominic H. Moon, Jenq‐Haur Wang, et al.. (2025). Contour uncertainty assessment for MD-omitted daily adaptive online head and neck radiotherapy. Radiotherapy and Oncology. 205. 110707–110707.
4.
Yuan, Chunying, Chunming Gao, Ti Bai, et al.. (2024). Biologically synthesized 10-Hydroxy-2-Decenoic acid activates cornified envelope formation via PPARα signaling, and regulates oxidative respiratory chain to improve mitochondrial function. Journal of Functional Foods. 122. 106505–106505. 1 indexed citations
5.
6.
Liang, Xiao, Michael Dohopolski, Mu‐Han Lin, et al.. (2024). Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy. Physics and Imaging in Radiation Oncology. 31. 100610–100610. 2 indexed citations
7.
Balagopal, Anjali, Michael Dohopolski, Young Suk Kwon, et al.. (2024). Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer. Physics and Imaging in Radiation Oncology. 30. 100577–100577. 1 indexed citations
8.
Bai, Ti, Dominic H. Moon, Arnold Pompoš, et al.. (2024). Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer. Practical Radiation Oncology. 15(2). e211–e219. 2 indexed citations
9.
Dohopolski, Michael, Ti Bai, Junjie Wu, et al.. (2024). Performance deterioration of deep learning models after clinical deployment: a case study with auto-segmentation for definitive prostate cancer radiotherapy. Machine Learning Science and Technology. 5(2). 25077–25077. 1 indexed citations
10.
Liang, Xiao, Allen Yen, Ti Bai, et al.. (2023). Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR‐only radiotherapy. Medical Physics. 50(12). 7368–7382. 4 indexed citations
11.
Liang, Xiao, Howard E. Morgan, Ti Bai, et al.. (2023). Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy. Physics in Medicine and Biology. 68(4). 45012–45012. 7 indexed citations
12.
Xiao, Haonan, Ti Bai, Bing Li, et al.. (2023). 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. 43(1). 162–174. 4 indexed citations
13.
Shao, Hua‐Chieh, Jing Wang, Ti Bai, et al.. (2022). Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling. Physics in Medicine and Biology. 67(11). 115009–115009. 27 indexed citations
14.
Liang, Xiao, Jaehee Chun, Howard E. Morgan, et al.. (2022). Segmentation by test‐time optimization for CBCT‐based adaptive radiation therapy. Medical Physics. 50(4). 1947–1961. 7 indexed citations
15.
16.
Li, Wen, Samaneh Kazemifar, Ti Bai, et al.. (2021). Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning. Biomedical Physics & Engineering Express. 7(2). 25020–25020. 17 indexed citations
17.
Bai, Ti, Dan Nguyen, Bao Wang, et al.. (2021). Deep Interactive Denoiser (DID) for X-Ray Computed Tomography. IEEE Transactions on Medical Imaging. 40(11). 2965–2975. 3 indexed citations
18.
Bai, Ti, Hao Yan, Xun Jia, et al.. (2017). Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning. IEEE Transactions on Medical Imaging. 36(12). 2466–2478. 31 indexed citations
19.
Xu, Yuan, Ti Bai, Hao Yan, et al.. (2015). A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy. Physics in Medicine and Biology. 60(9). 3567–3587. 97 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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