Effect of Respiratory Motion on Lung Counting Efficiency Using a 4D NURBS-Based Cardio-Torso (NCAT) Phantom
Bibliographic record
Abstract
The Human Monitoring Laboratory (Canada) has looked at parameters (lung volume, lung deposition pattern, etc.) that can affect the counting efficiency of its lung counting system. The calibration of the system is performed using the Lawrence Livermore National Laboratory (LLNL) torso phantom; however, the effect of respiratory motion cannot be accounted for using these phantoms. When measuring an internal deposition in the lungs of a subject, respiration causes a change in the volume of the lungs and the thoracic cavity and introduces a variable distance between the lungs and the detectors. These changes may have an impact on the counting efficiency and may need to be considered during a measurement. In this study, the HML has simulated the respiration motion using a 4D non-uniform rational b-spline (NURBS)-based Cardiac-Torso (NCAT) phantom and determined the impact of that motion on the counting efficiency of their lung counting system during measurement. The respiratory motion was simulated by a 16 timeframe cycled 4D NURBS-based NCAT phantom developed at the Department of Biomedical Engineering and Radiology, University of North Carolina. The counting efficiency of the four germanium detectors comprising the HML lung counting system was obtained using MCNPX version 2.6E for photon energies between 17 and 1,000 keV. The amount of uncertainty due to the breathing motion was estimated by looking at the efficiency bias, which was highest at low photon energies as expected due to attenuation and geometry effects. Also, to reduce the influence of the detectors' positioning, an array was calculated by adding the individual detector tallies for a given energy and timeframe. For photon energies of 40 keV and higher, the array efficiency bias showed an underestimation of about 5%. If compared to other parameters already studied by the HML, this value demonstrates the insignificant impact of the breathing motion.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".