List-mode motion tracking for positron emission tomography imaging using low-activity fiducial markers
Bibliographic record
Abstract
Motion correction in positron emission tomography (PET) imaging may benefit from an accurate knowledge of the patient motion during the image acquisition. We evaluated the feasibility to detect and record the motion of external fiducial markers during PET scans. We developed an iterative algorithm that can track the three-dimensional (3D) motion of low-activity fiducial markers while surrounded by high physiological tracer activity from a patient undergoing PET imaging. Monte Carlo techniques were used to simulate a 92.5-kBq22Na marker moving sinusoidally in 3D. The simulated events were combined with list-mode data from patients undergoing cardiac PET imaging in order to test the algorithm. In experimental studies, three external22Na markers were placed on a dynamic torso phantom with an initial activity of approximately 680 MBq of82Rb in its cardiac insert. We tracked the motion of those markers while simulating breathing motion and patient drift with the phantom. Results from simulations show that a 92.5-kBq marker can be tracked in 3D at a frequency of 2 Hz with an accuracy of 1.2 mm and a precision of 0.8 mm. The phantom study qualitatively confirms that the algorithm can track both breathing and patient motion. The relative accuracy of the tracking was 0.4±1.1 mm and the precision was 0.8 mm. In conclusion, we have developed an algorithm that can track the 3D motion of low-activity positron-emitting markers during PET imaging. This motion information might prove useful in developing new motion correction schemes in clinical PET imaging.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".