Application of Three-Dimensional Motion Tracking of Low-Activity Fiducial Positron-Emitting Markers in Radiation Therapy and Positron Emission Tomography
Bibliographic record
Abstract
Patient body motion limits the delivery accuracy of radiotherapy and creates blurring artefacts on positron emission tomography (PET) images.Those adverse effects can be mitigated by tracking the patient body motion and using the information appropriately.A technique that can track the three-dimensional motion of low-activity positron-emitting fiducial markers was developed.The application of this tracking technique, called PeTrack, for respiratory-gated radiotherapy and for motion-compensated PET imaging was evaluated.The feasibility of respiratory-gated radiotherapy using PeTrack was assessed.A respiratory gating interface was developed in LabVIEW to communicate with an Elekta Precise research linear accelerator and toggle the delivery of the beam.Radiochromic films were placed in a rod insert of an anthropomorphic dynamic thorax phantom to evaluate the dose distribution of the gated and non-gated delivery of a small square beam.A single low-activity source was used to track the motion of the phantom.Real patient breathing data were used as the basis of the motion of the phantom.Visual and quantitative assessments of the films confirmed that respiratory-gated radiotherapy using real-time tracking based on positron-emitting fiducial markers is achievable.The blurring of the dose distribution due to motion was greatly reduced on the gated deliveries compared to the non-gated cases.A modified version of the tracking algorithm was developed to track fiducial markers when high physiological tracer activity from a patient undergoing PET imaging First of all, I need to express my sincerest gratitude to my supervisor, Dr. Tong Xu.He has been an incredible mentor and supervisor.I am eternally grateful for his unwavering support and guidance throughout my almost nine (!) years at his side.He is without a doubt the smartest person I have had the pleasure of knowing.His kindness, patience, positive attitude, and sense of humour have helped me get where I am today.I am also grateful to all the people who discussed technical issues and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".