A novel automatic real-time motion tracking method in MRI-guided radiotherapy using enhanced Tracking-Learning-Detection framework with automatic segmentation.
5 affiliations
- 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
- 2Department of Radiation Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan South Road, Chaoyang District, Beijing, 100021, China.
- 3Department of Radiation Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan South Road, Chaoyang District, Beijing, 100021, China. [email protected].
- 4School of Electronic Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing, 100876, China. [email protected].
- 5Department of Radiation Oncology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuan South Road, Chaoyang District, Beijing, 100021, China. [email protected].
Abstract
Background and purpose
Accurate motion tracking in magnetic resonance imaging-guided radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework combined with automatic segmentation, eliminating the need for pre-training.Methods
We developed a novel motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese (ICV) model, termed ETLD + ICV. The ETLD framework was upgraded for real-time MRIgRT, including search process optimization, an enhanced median-flow tracker, and dynamic detection region adjustments. It receives 3.5D MRI data as input and outputs the location prediction of the target volume for each frame. Based on this, ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with optimized key parameters. The method requires no pre-training and was tested on 3.5D MRI scans from 10 patients with liver metastases. No-reference image quality assessment and manual verification were used to filter out low-quality image data. Comprehensive statistical analyses were performed to assess the statistical significance of differences between experimental outcomes (p < 0.05). In addition, comparative experiments based on an external benchmark dataset were conducted to further evaluate the performance of the proposed method.Results
Evaluation across 106,000 frames from 77 treatment fractions demonstrated sub-millimeter tracking errors of less than 0.8 mm, with over 99% precision and 98% recall for all patients in the Beam Eye View (BEV)/Beam Path View (BPV) orientation. The ETLD + ICV method achieved a Dice global score of more than 82% for all patients, demonstrating the method's extensibility and precise target volume coverage. Comparisons based on the external dataset further showed that the proposed method achieved a balanced trade-off between accuracy and efficiency.Conclusion
This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments.
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BibTeX
@article{chen2026novel,
title = {{A novel automatic real-time motion tracking method in MRI-guided radiotherapy using enhanced Tracking-Learning-Detection framework with automatic segmentation.}},
author = {Shengqi Chen and Zilin Wang and Jianrong Dai and Shirui Qin and Ying Cao and Ruiao Zhao and Jiayun Chen and Guohua Wu and Yuan Tang},
journal = {Radiation oncology (London, England)},
year = {2026},
month = aug,
doi = {10.1186/s13014-026-02901-1},
url = {https://europepmc.org/article/MED/42928203},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A novel automatic real-time motion tracking method in MRI-guided radiotherapy using enhanced Tracking-Learning-Detection framework with automatic segmentation.
AU - Shengqi Chen
AU - Zilin Wang
AU - Jianrong Dai
AU - Shirui Qin
AU - Ying Cao
AU - Ruiao Zhao
AU - Jiayun Chen
AU - Guohua Wu
AU - Yuan Tang
PY - 2026
DA - 2026/08/01
JO - Radiation oncology (London, England)
DO - 10.1186/s13014-026-02901-1
UR - https://europepmc.org/article/MED/42928203
AB - Background and purpose Accurate motion tracking in magnetic resonance imaging-guided radiotherapy (MRIgRT) is essential for effective treatment delivery. This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework combined with automatic segmentation, eliminating the need for pre-training.Methods We developed a novel motion tracking and segmentation method by integrating the ETLD framework with an improved Chan-Vese (ICV) model, termed ETLD + ICV. The ETLD framework was upgraded for real-time MRIgRT, including search process optimization, an enhanced median-flow tracker, and dynamic detection region adjustments. It receives 3.5D MRI data as input and outputs the location prediction of the target volume for each frame. Based on this, ICV was used for precise target volume coverage, refining the segmented region frame by frame using tracking results, with optimized key parameters. The method requires no pre-training and was tested on 3.5D MRI scans from 10 patients with liver metastases. No-reference image quality assessment and manual verification were used to filter out low-quality image data. Comprehensive statistical analyses were performed to assess the statistical significance of differences between experimental outcomes (p < 0.05). In addition, comparative experiments based on an external benchmark dataset were conducted to further evaluate the performance of the proposed method.Results Evaluation across 106,000 frames from 77 treatment fractions demonstrated sub-millimeter tracking errors of less than 0.8 mm, with over 99% precision and 98% recall for all patients in the Beam Eye View (BEV)/Beam Path View (BPV) orientation. The ETLD + ICV method achieved a Dice global score of more than 82% for all patients, demonstrating the method's extensibility and precise target volume coverage. Comparisons based on the external dataset further showed that the proposed method achieved a balanced trade-off between accuracy and efficiency.Conclusion This study successfully developed an automatic real-time markerless motion tracking method for MRIgRT. The novel method not only delivers exceptional precision in tracking and segmentation but also shows enhanced adaptability to clinical demands, making it an indispensable asset in improving the efficacy of radiotherapy treatments.
SN - 1748-717X
ER -