SU‐FF‐J‐29: Preliminary Experimental Study for Tumor Position Tracking During Radiotherapy Using Positron Emission Markers
Bibliographic record
Abstract
Purpose: To experimentally evaluate the feasibility of tumor position tracking using positron emission markers for external beam radiation therapy. Method and Materials: Clinical PET imaging is known to be limited by its low spatial resolution of approximately 4–10 mm. However, if the geometry of the source is known, the position of the sources can be determined with sub‐millimeter accuracy. We propose to apply this concept for real‐time tumor position tracking during radiotherapy. For pulmonary and abdominal tumors, delivering accurate radiation therapy is limited by the motion of the tumor as the patient breathes. By implanting positron emission markers into tumor, and using pairs of position‐sensitive detectors to track the resulting annihilation gamma rays, the position of the tumor can be tracked in real‐time with high accuracy. Positron emission based technique will deliver a lower radiation dose to normal tissue than x‐ray fluoroscopy, and the smaller size of the positron emission markers reduces risk to the patient during implantation. The concept and simulation study has been previously published (T. Xu et.al, Med. Phys. 33(7) p2598). In this feasibility study, the technique was evaluated using a clinical PET system (CTI ECAT). A Ge‐68 Linear source was used to simulate a point source in 2D slices during conventional 2D acquisition. The Ge‐68 source was put at 3 locations and the data were combined to simulate multiple (3) markers. Results: Three point sources were successfully localized using proposed technique. The localization precision ranges from 4 mm down to 0.2 mm when the number of coincident events used increases from 100 to 4100. The precession reaches 0.77 mm with only about 200 events from three point sources. Conclusion: The study has shown that multiple positron emission markers can be localized with sub‐millimeter precision, which is sufficient for tumor localization during radiotherapy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".