Poster — Thur Eve — 11: Evaluation of the Performance of a Positron Emission Based Tumour‐Tracking Technique
Bibliographic record
Abstract
The delivery accuracy of radiotherapy treatments remains limited by tumor motion due to patient breathing. We present a simulation study and experimental evaluation of a technique called PeTrack that can track tumour location in real‐time. Position sensitive detectors record annihilation coincidence events from fiducial positron emission markers implanted in or around the tumour. It uses an expectation‐maximization clustering algorithm to track the position of the markers and a linear extrapolation method for motion prediction. We assessed the performance of the tracking using a clinical positron emission tomography system with the markers moving in different patterns. We also evaluated the performance of the tracking for stationary markers using a prototype PeTrack detector. In the experimental study with the PET scanner, the data was fitted to two theoretical curves. The root mean square error (RMSE) was 0.43 mm in x and 0.46 mm in y for a sinusoidal movement pattern. The RMSE was 0.64 mm in x for motion following animal breathing data. The linear extrapolation method for motion prediction yielded an average prediction error of 1.1 mm in the experimental study, with a prediction error of 2.3 mm at a 95% confidence level. Using the prototype PeTrack detector, the tracking precision was found to be 0.16 mm in x, 0.20 mm in y and 0.21 mm in z. We conclude that PeTrack can track tumour motion in real‐time and improve the delivery accuracy of radiotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".