Cascade analysis for medical imaging detectors with stages involving both amplification and dislocation processes
Bibliographic record
Abstract
Cascade analysis is a powerful tool which can be used to calculate the signal and noise properties of medical imaging detectors. It involves the conceptual separation of the imaging chain into stages which consist of either pure amplification or pure dislocation stages. It is, however, not always possible to break the physical processes down to these elementary stages. In this work we derive a new cascade equation which is applicable to any stage which involves multiple amplifications and dislocations. The equation simplifies to the known equations for pure amplification and pure dislocation stages in the appropriate limits, and can be numerically calculated using Monte Carlo techniques for more complicated situations. We demonstrate the use of this equation with an example: we derive an expression for the DQE of a metal/phosphor detector for megavoltage imaging with our formalism, and evaluate the expression with Monte Carlo techniques. We have found that there is excellent agreement between theory and experimental results, and believe that the formalism could be useful for other applications where the amplification and dislocation processes cannot be divided into elementary stages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".