Monte Carlo Simulations of a Non-Invasive Positron Detector to Measure the Arterial Input Function for Dynamic PET
Bibliographic record
Abstract
Abstract This work presents Monte Carlo (MC) study of a novel non-invasive positron detector, hereinafter called NID, designed to measure the arterial input function (AIF) through the wrist of a patient for use with dynamic positron emission tomography (PET). The goal of the study was to optimize a previously developed NID prototype, to determine its efficiency and ability to distinguish between the arterial and venous portions of the signal escaping a wrist phantom. A user code based on the Geant4 MC toolkit was developed to model the NID and a wrist phantom. The scintillator based detectors were modelled as 64 polystyrene cylinders, 0.97 mm in diameter, 10 cm long and capped with cylindrical photomultiplier tubes. The scintillator fibers were arranged in a single band around a 64.13 mm polyethylene cylinder representing a model of the wrist. Two cylinders, 2.30 mm in diameter were placed 6 mm apart, 2 mm below the surface of the wrist phantom representing the radial artery and vein. Two simulations were performed by placing 100 million decay events of oxygen-15 (15O) or fluorine-18 (18F), randomly distributed between the artery and vein. Visible wavelength photon tracking was enabled, and Photomultiplier tubes were simulated to collect the visible photons. Deposited energy per event and location of energy deposition were calculated. A python algorithm was used to analyse the results. The arterial signal produced a 5.28 mm and 10.32 mm FWHM for 15O and 18F respectively. The algorithm could determine the correct location of interaction 98% of the time. The NID can resolve the arterial and venous signal and is thus suitable for determining the AIF for dynamic PET.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".