Fast, accurate and versatile Monte Carlo method for computing system matrix
Bibliographic record
Abstract
A new methodology is proposed to mitigate the high computation cost required to derive accurate Monte Carlo (MC) based system matrix for tomographic image reconstruction. The strategy consists of taking advantage of the symmetries between the lines of response to increase the statistics of data collected for the determination of the system matrix coefficients. By using the rotation and axial symmetries of a cylindrical camera, the number of MC generated events can be reduced substantially without affecting the matrix coefficient accuracy. Moreover, using the GATE simulator list-mode saving capabilities for storing coincidence events, single events and/or single hits with all their relevant information, the MC simulation can be performed only once and system matrices for different system configurations be derived from the same simulation. Using for example Positron emission tomography (PET), the processing of the collected MC data can be fine tuned to the imaging system characteristics by setting accordingly the time and energy blurring, the detector efficiencies, the coincidence time window width and the energy thresholds. The system matrix can also include or exclude PET events like scatters and randoms. Furthermore, the system matrix can be computed for different image grids and basis functions without requiring a new MC simulation to be performed.
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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 | 0.000 |
| 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 | 0.000 | 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".