Theoretical optimization of dual-energy x-ray imaging of chronic obstructive pulmonary disease (COPD)
Bibliographic record
Abstract
We propose two-dimensional (2D) dual-energy (DE) x-ray imaging of lung structure and function for the assessment of COPD, and investigate the resulting image quality theoretically using the human observer detectability index (d') as a figure of merit. We modeled the ability of human observers to detect ventilation defects in xenon enhanced DE (XeDE) images and emphysema in unenhanced DE images. Our model of d' accounted for the extent of emphysematous destruction and functional impairment as a function of defect/lesion contrast, spatial resolution, x-ray scatter, quantum and background anatomical noise power spectrum (NPS), and the efficiency of human observers. The effect of x-ray spectrum and exposure allocation factor on d' was also explored. Our results suggest that, the detectability is maximized for exposure allocation factors that minimize quantum NPS. The optimal combination of tube voltage was found to be ~50/140 kV or 60/140 kV depending on the task and patient at an x-ray exposure equal to that of a standard chest x-ray. In 2D DE x-ray imaging of COPD, the detectability is primarily limited by low contrast, x-ray scatter, and anatomic noise, the latter two of which reduce the detectability of individual defects by 30% and ~>90%, respectively.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".