Optimal kVp selection for dual‐energy imaging of the chest: Evaluation by task‐specific observer preference tests
Bibliographic record
Abstract
Human observer performance tests were conducted to identify optimal imaging techniques in dual-energy (DE) imaging of the chest with respect to a variety of visualization tasks for soft and bony tissue. Specifically, the effect of kVp selection in low- and high-energy projection pairs was investigated. DE images of an anthropomorphic chest phantom formed the basis for observer studies, decomposed from low-energy and high-energy projections in the range 60-90 kVp and 120-150 kVp, respectively, with total dose for the DE image equivalent to that of a single chest radiograph. Five expert radiologists participated in observer preference tests to evaluate differences in image quality among the DE images. For visualization of soft-tissue structures in the lung, the [60/130] kVp pair provided optimal image quality, whereas [60/140] kVp proved optimal for delineation of the descending aorta in the retrocardiac region. Such soft-tissue detectability tasks exhibited a strong dependence on the low-kVp selection (with 60 kVp providing maximum soft-tissue conspicuity) and a weaker dependence on the high-kVp selection (typically highest at 130-140 kVp). Qualitative examination of DE bone-only images suggests optimal bony visualization at a similar technique, viz., [60/140] kVp. Observer preference was largely consistent with quantitative analysis of contrast, noise, and contrast-to-noise ratio, with subtle differences likely related to the imaging task and spatial-frequency characteristics of the noise. Observer preference tests offered practical, semiquantitative identification of optimal, task-specific imaging techniques and will provide useful guidance toward clinical implementation of high-performance DE imaging systems.
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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 | 0.015 | 0.062 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".