SU‐GG‐I‐109: Using EGSnrc Within GATE to Improve the Efficiency Of positron Emission Tomography Simulations
Bibliographic record
Abstract
Purpose: GATE, a Geant4 based application for use in emission tomography, is a powerful package that permits realistic Monte Carlo simulations of both, the scanner geometry and the digitization chain. The main problem when using GATE in practice is its slow simulation speed. The purpose of this investigation is to improve the efficiency of PET related simulations. Method and Materials: An EGSnrc based radiation transport tool, referred to as egs_pet, is developed. egs_pet can be used together with GATE in two modes: In mode 1, egs_pet performs the simulation of radiation transport in the phantom and passes exiting particles to GATE for further transport through the scanner geometry and for digitization. In mode 2, egs_pet performs the entire simulation and writes energy depositions in the detectors to a file. GATE is modified to be able to read this file when performing the digitization. Results: The correct operation of egs_pet within GATE is validated using benchmark calculations of a source within a water phantom and a detailed model of the GE Advance PET scanner. In mode 1, the number of singles and coincidences are found to agree with GATE within the statistical uncertainties (0.2% for singles). Differences of about 0.5% are observed in mode 2, which can be attributed to differences between Geant4 and EGSnrc when modeling binding effects for photon interactions in BGO. For a 4 mm voxel phantom, the simulation efficiency is increased by a factor of 4 in mode 1, and by a factor of 130 or 44 with digitization excluded or included in mode 2. Conclusion: A new Monte Carlo tool for PET simulations based on EGSnrc is developed and incorporated into GATE. Significant gains in simulation efficiency are achieved. Results from egs_pet simulations agree with GATE at the 1% level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".