Validation of complex cascaded models of medical imaging systems by Monte Carlo
Bibliographic record
Abstract
Cascaded models have been used by a number of investigators to derive analytic expressions for the Wiener noise power spectrum (NPS) and detective quantum efficiency (DQE) based on design parameters to evaluate the performance of medical x-ray imaging systems. These analytic models are required to establish operating benchmarks and compare the performance of real detectors. Although application of the cascaded approach has had several successes, its contribution is often limited when applied to complex models. This is due to the fact that while final algebraic expressions can be relatively simple, the cascaded approach involves the manipulation of many hundreds of terms. To overcome this limitation a computational engine has been developed using Matlab's Simulink and symbolic math capabilities. Based on a recursive programming approach, this engine generates analytic expressions of NPS and DQE for cascaded models of arbitrary complexity. In order to validate the resulting expressions, a Monte Carlo (MC) simulation program has been developed that performs an analysis based on C-code generated by the computational engine for each model. The Monte Carlo code generates an incident quantum image as a Poisson distribution of quanta. This distribution is passed through appropriate serial and parallel cascades of modules representing elementary processes and is used to calculate the NPS for comparison with the analytic NPS. Results show excellent agreement between Monte Carlo and theoretical expressions. We are at the stage where complex cascaded modelling is becoming practical tool in the design of new detector systems.
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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 | 0.001 |
| Open science | 0.001 | 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".