Theoretical feasibility of dual‐energy radiography for structural and functional imaging of chronic obstructive pulmonary disease
Bibliographic record
Abstract
Purpose Chronic obstructive pulmonary disease (COPD) affects ∼200 million people worldwide. 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. Methods We investigated xenon‐enhanced DE (XeDE) radiography for functional imaging of COPD and unenhanced DE radiography for structural imaging of COPD. We modeled the ability of human observers to detect ventilation defects in XeDE images and emphysema in (unenhanced) DE images using the detectability index ( ) as a figure of merit. We accounted for the extent of emphysematous destruction and functional impairment as a function of disease severity, defect/lesion contrast, spatial resolution, x‐ray scatter, quantum noise, anatomic noise, and the efficiency of human observers. Whether or not disease was detectable was determined based on a detectability threshold of two. For (unenhanced) DE imaging of emphysema, we compared detectability with that of single‐energy (SE) imaging. Models of signal and noise were compared to published data. Results Models of signal and noise agreed well with published data, and model predictions of the detectability of emphysema by SE radiography were consistent with poor sensitivity (i.e., ) to mild to moderate COPD but moderate sensitivity (i.e., ) to severe COPD. The detectability of emphysema by DE radiography was greater than that of SE radiography, but did not cross the threshold of detectability for mild to moderate COPD. The detectability index for XeDE imaging exceeded the detectability threshold for mild, moderate, and severe COPD. Conclusions Dual‐energy radiography may offer modest improvements in the detection of emphysema relative to SE imaging, but will unlikely enable detecting mild and moderate COPD. However, XeDE radiography may enable detection of functional abnormalities associated with mild, moderate and severe COPD at x‐ray exposures typical of those used in conventional chest radiography, thus warranting further investigation as a low‐dose, low‐cost alternative to CT‐ and MRI‐based approaches for functional imaging of COPD.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".