WE‐G‐110‐06: Introduction to the AAPM Task Group No. 195 ‐ Monte Carlo Reference Data Sets for Imaging Research
Bibliographic record
Abstract
Purpose: To develop reference data sets of common Monte Carlo simulations in medical imaging research for use by researchers who are learning how to implement Monte Carlo simulations or needing to validate their own Monte Carlo simulations. Methods: Complete implementation specifications and results for different Monte Carlo simulations reflecting common medical imaging research conditions were developed. The specifications include: full geometry details, material compositions, particle energies, spectral definitions and scoring details. The specifications for each of these simulations will be implemented in five different Monte Carlo software packages: EGSnrc, Geant4, MCNP, Penelope and Sierra. Results: A total of eight commonly performed Monte Carlo simulations for five different imaging applications were developed: (i) production of x‐rays, (ii) half‐value layer estimation, (iii) radiographic dosimetry, (iv) x‐ray scatter in radiography, (v) mammographic dosimetry, (vi) x‐ray scatter in mammography, (vii) CT dosimetry of simple volumes and (viii) CT dosimetry of voxelized volumes. The simulation conditions and the results for all five implementations will be published, in detail, in the Task Group report and will be made available online in the AAPM website. Once published, we will invite scientists to submit their results when simulating these conditions. Conclusion: The publication of reference data sets for common Monte Carlo simulations will be a useful tool for imaging researchers new to the field of computer simulations or seeking to validate their newly‐developed simulations before performing new experiments. Upon successful completion of this initial data set, we envision the extension of this work to include other modalities, such as nuclear medicine imaging.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".