Quantitative comparison using generalized relative object detectability (G-ROD) metrics of an amorphous selenium detector with high resolution microangiographic fluoroscopes (MAF) and standard flat panel detectors (FPD)
Bibliographic record
Abstract
m bias field. A simulated detector DQE was determined, and used in comparative calculations of the Relative Object Detectability (ROD) family of prewhitening matched-filter (PWMF) observer and non-prewhitening matched filter (NPWMF) observer model metrics to gauge a-Se detector performance against existing high resolution micro-angiographic fluoroscopic (MAF) detectors and a standard flat panel detector (FPD). The PWMF-ROD or ROD metric compares two x-ray imaging detectors in their relative abilities in imaging a given object by taking the integral over spatial frequencies of the Fourier transform of the detector DQE weighted by an object function, divided by the comparable integral for a different detector. The generalized-ROD (G-ROD) metric incorporates clinically relevant parameters (focal-spot size, magnification, and scatter) to show the degradation in imaging performance for detectors that are part of an imaging chain. Preliminary ROD calculations using simulated spheres as the object predicted superior imaging performance by the a-Se detector as compared to existing detectors. New PWMF-G-ROD and NPWMF-G-ROD results still indicate better performance by the a-Se detector in an imaging chain over all sphere sizes for various focal spot sizes and magnifications, although a-Se performance advantages were degraded by focal spot blurring. Nevertheless, the a-Se technology has great potential to provide breakthrough abilities such as visualization of fine details including of neuro-vascular perforator vessels and of small vascular devices.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".