Validation of PET-SORTEO Monte Carlo simulations for the geometries of the MicroPET R4 and Focus 220 PET scanners
Bibliographic record
Abstract
PET-SORTEO is a Monte Carlo-based simulator that enables the fast generation of realistic PET data for the geometry of the ECAT EXACT HR+ scanner. In order to address the increasing need for simulation models of animal PET imaging systems, our aim is to adapt and configure this simulation tool for small animal PET scanners, especially for the widely distributed microPET R4 and Focus 220 systems manufactured by Siemens Preclinical Solutions. We propose a simulation model that can produce realistic rodent images in order to evaluate and optimize acquisition and reconstruction protocols. The first part of this study presents the validation of SORTEO against the geometries of the R4 and the Focus 220 systems. This validation is carried out against actual measurements performed on the R4 scanner at the Montreal Neurological Institute in Canada and on the Focus 220 system of the department of radiopharmaceuticals of the Austrian Research Center in Seibersdorf. The comparison of simulated and experimental performance measurements includes spatial resolution, energy spectra, scatter fraction and count rates. In the second part of the study, we demonstrate the ability to rapidly generate realistic whole-body radioactive distributions using the MOBY phantom and give comparative example case studies of the same rodent model simulated with PET-SORTEO for the R4 and Focus 220 systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".