Experimental optimization of the exposure allocation for xenon-enhanced dual-energy x-ray imaging of lung function
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by persistent airflow limitation resulting from emphysema and small airway disease. In our recent work we proposed xenon-enhanced dual-energy (XeDE) radiography for functional imaging of COPD. Using mathematical models, we showed that XeDE radiography has the potential to enable detecting functional abnormalities associated with early-stage COPD. The purpose of this study is to investigate the optimal exposure allocation for XeDE X-ray imaging of lung function by experiment and to validate the predictions of our theoretical model. Experiments were conducted using a custom-built chest phantom representing an adult female chest and containing a simulated ventilation defect. The phantom was imaged using a CsI/CMOS energy integrating X-ray detector (XINEOS-3030HS, Teledyne DALSA - Professional Imaging, Ontario, Canada) with a 151.8 µm pixel pitch. The low-energy (LE) and highenergy (HE) tube voltages were fixed at 60 kV and 140 kV respectively. We define the exposure allocation factor (f ) as the ratio of HE entrance exposure [Roentgens] to the LE entrance exposure. The value of (f ) was varied from 0.25 to 2 while keeping the total entrance exposure fixed at ∼60 mR. We used contrast-to-noise ratio (CNR) normalized by the square root of total entrance exposure as a figure of merit. Our theoretical model of CNR accounted for the contrast of ventilation defects, quantum noise and X-ray scatter. Quantum noise was calculated using cascaded system analysis accounting for the quantum efficiency, K fluorescence, optical collection efficiency, optical blur and noise aliasing. Our models of defect contrast, noise and CNR agreed well with experimental results. The theoretical and experimental results show that the optimal exposure allocation is f = 0.5 indicating that ∼2/3 of the total exposure should be allocated to LE image.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".