Elimination of ghost markers during dual sensor‐based infrared tracking of multiple individual reflective markers
Bibliographic record
Abstract
The accuracy of dose delivery in radiotherapy is affected by the uncertainty in tumor localization. Motion of internal anatomy due to physiological processes such as respiration may lead to significant displacements which compromise tumor coverage and generate irradiation of healthy tissue. Real-time tracking with infrared-based systems is often used for tracking thoracic motion in radiation therapy. We studied the origin of ghost markers ("crosstalk") which may appear during dual sensor-based infrared tracking of independent reflective markers. Ghost markers occur when two or more reflective markers are coplanar with each other and with the sensors of the two camera-based infrared tracking system. Analysis shows that sensors are not points but they have a finite extent and this extent determines for each marker a "ghost volume." If one reflective marker enters the ghost volume of another marker, ghost markers will be reported by the tracking system; if the reflective markers belong to a surface their "ghost volume" is reduced to a "ghost surface" (ghost zone). Appearance of ghost markers is predicted for markers taped on the torso of an anthropomorphic phantom. This study illustrates the dependence of the shape, extent, and location of the ghost zones on the shape of the anthropomorphic phantom, the angle of view of the tracking system, and the distance between the tracking system and the anthropomorphic phantom. It is concluded that the appearance of ghost markers can be avoided by positioning the markers outside the ghost zones of the other markers. However, if this is not possible and the initial marker configuration is ghost marker-free, ghost markers can be eliminated during real-time tracking by virtue of the fact that they appear in the coordinate data sequence only temporarily.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".