Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy
Open accessAbstract
Radiation therapy is a local treatment aimed at killing cells in and around a tumor. Accurate predictions of lung tumor motion help to improve the precision of radiation treatment by controlling the position of a patient during radiation treatment. Our goal is to develop an algorithmic solution for predicting the position of a target in 3D in real time. In addition to prediction accuracy and low fluctuation of the prediction signal (jitter) we aim for minimum calibration time each patient at the beginning of the procedure. Our solution is based on a model form from the family of exponential smoothing. Performance is evaluated on clinical datasets capturing different behavior (quiet, talking, laughing), and validated in real-time on a prototype with respiratory motion imitation. Proposed solution (ExSmi) achieves good accuracy of prediction (error 4-9 mm/s) with tolerable jitter values (5 -- 7 mm/s). The solution performs well to be prototyped and deployed in applications of radiotherapy.
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Registration is of great significance in radiotherapy; it is performed to calculate the offset of the current target from the planned position, and the offset is used to adjust patient position or radiation beam.
“Moreover, registration is also of great significance in radiotherapy (11, 12); it is performed to calculate the offset of the current target from the planned position, and the offset determined from the registration is used to adjust the patient position or the radiation beam.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [11]
The Introduction describes determining a tumour’s current position and compensating for motion by repositioning the beam; the Abstract also mentions controlling the couch or beam. Neither passage describes registration to calculate an offset from the planned position, so the claim’s registration-specific explanation is unsupported. (LLM, conf=0.85)
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Research works printed but not linked: 17
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Cited by
Sorted by citations
A review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works
“Moreover, registration is also of great significance in radiotherapy (11, 12); it is performed to calculate the offset of the current target from the planned position, and the offset determined from the registration is used to adjust the patient position or the radiation beam.”
Background
- Authors
- Krilavicius, T., Zliobaite, I., Simonavicius, H., & Jaruevicius, L.
- Venue
- 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 7-12
- ISSN
- 1063-7125 (ISSN-L)
- OpenAlex
- W1962651629
- Cited by
- 15
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 1 · 2019: 1 · 2018: 2 · 2017: 1
- FWCI
- 3.48
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE Symposium on Computer-Based Medical Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Vytautas Magnus University (submittedVersion)
- arXiv (Cornell University)
- Funders
- EyeSight Foundation of Alabama
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- respiratory motion prediction, lung tumor tracking, real-time tracking, radiotherapy, exponential smoothing
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