Theoretical feasibility of dual-energy functional x-ray imaging of respiratory disease
Bibliographic record
Abstract
We propose a two-dimensional (2D) contrast-enhanced dual-energy (DE) approach for functional x-ray imaging of respiratory disease. With this approach, non-radioactive xenon is used to provide contrast between ventilated regions of the lung and unventilated regions of the lung (i.e. ventilation defects); DE subtraction is used to suppress rib structures from 2D thoracic images. We modeled theoretically the signal-to-noise ratio (SNR) and area under the receiver operating characteristic curve (AUC) of a human observer for a defect present vs. defect absent binary classification task under signal-known-exactly/background-known-exactly conditions. Our model accounted for the size of ventilation defects, contrast of ventilation defects, quantum noise, finite spatial resolution, x-ray attenuation and observer efficiency. We modeled spherical defects with diameters up to 2.5 cm, and contrast and noise levels relevant for imaging of children, adolescents, adult males and adult females. Quantum noise and spatial resolution properties were calculated assuming an ideal energy-integrating x-ray detector. All calculations were performed assuming low-energy and high-energy applied tube voltages of 70 kV and 140 kV, respectively, with 2 mm of added copper filtration on the high-energy spectrum, and a total entrance exposure of 18 mR, which is typical for anterior-posterior thoracic imaging procedures. Our analysis shows that an AUC of 0.85 can be achieved for defect diameters as small as 1.1 cm, 1.2 cm, 1.3 cm and 1.4 cm for children ages 2 to 8, adolescents ages 9 to 14, adult males and adult females, respectively. Our results suggest that the DE approach proposed here warrants further investigation as a low-dose, low-cost alternative to existing approaches for functional imaging of respiratory disease.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".