Performance evaluation of real‐time motion tracking using positron emission fiducial markers
Bibliographic record
Abstract
PURPOSE: Tumor motion due to patient breathing is a factor that limits the accuracy of dose distribution in radiotherapy. One of the methods to improve the accuracy is by applying respiratory gating or tumor tracking. Both techniques require a precise determination of the spatial location of the tumor. We present an experimental evaluation of the performance of PeTrack, a technique that can track internal fiducial markers in real-time for tumor tracking. METHODS: PeTrack uses position sensitive detectors to record annihilation coincidence gamma rays from fiducial positron emission markers implanted in or around the tumor. It uses an expectation-maximization clustering algorithm to track the position of the markers. A normalized least mean square adaptive filter was used to predict the position of the markers 100 and 200 ms in the future. We evaluated the performance of the tracking and of the prediction by using a dynamic anthropomorphic thorax phantom to generate three-dimensional (3D) motion of three fiducial markers. The algorithm was run with four different data sets. In the first run, the motion of the markers was based on a sinusoidal model of respiratory motion. Three additional runs were done with motion based on patient breathing data. RESULTS: In the case of the sinusoidal model, the average 3D root mean square error for all markers was 0.44 mm. For the three runs based on patient breathing data, the precision of the 3D localization was 0.49 mm. At a latency of 100 ms, the average 3D prediction error was 1.3 +/- 0.6 mm for the sinusoidal model and for the three patient breathing runs. At a latency of 200 ms, the average 3D prediction errors were 1.7 +/- 0.8 mm for the sinusoidal model and 1.4 +/- 0.7 mm for the breathing runs. CONCLUSIONS: We conclude that PeTrack can track multiple fiducial markers in real-time with an accuracy and precision smaller than 2 mm. PeTrack can have a direct application in tumor tracking for radiation therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".