NEQ and task in dual-energy imaging: from cascaded systems analysis to human observer performance
Bibliographic record
Abstract
The relationship between theoretical descriptions of imaging performance (Fourier-based cascaded systems analysis) and the performance of real human observers was investigated for various detection and discrimination tasks. Dual-energy (DE) imaging provided a useful basis for investigating this relationship, because it presents a host of acquisition and processing parameters that can significantly affect signal and noise transfer characteristics and, correspondingly, human observer performance. The detectability index was computed theoretically using: 1) cascaded systems analysis of the modulation transfer function (MTF), and noise-power spectrum (NPS) for DE imaging; 2) a Fourier description of imaging task; and 3.) integration of MTF, NPS, and task function according to various observer models, including Fisher-Hotelling and non-prewhitening with and without an eye filter and internal noise. Three idealized tasks were considered: sphere detection, shape discrimination (sphere vs. disk), and texture discrimination (uniform vs. textured disk). Using images of phantoms acquired on a prototype DE imaging system, human observer performance was assessed in multiple-alternative forced choice (MAFC) tests, giving an estimate of area under the ROC curve (AΖ). The degree to which the theoretical detectability index correlated with human observer performance was investigated, and results agreed well over a broad range of imaging conditions, depending on the choice of observer model. Results demonstrated that optimal DE image acquisition and decomposition parameters depend significantly on the imaging task. These studies provide important initial validation that the detectability index derived theoretically by Fourier-based cascaded systems analysis correlates well with actual human observer performance and represents a meaningful metric for system optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".