Optimal kVp selection for dual‐energy imaging of the chest: Evaluation by task‐specific observer preference tests
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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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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 it