Dual-energy imaging of the chest: Optimization of image acquisition techniques for the ‘bone-only’ image
Bibliographic record
Abstract
Experiments were conducted to determine optimal acquisition techniques for bone image decompositions for a prototype dual-energy (DE) imaging system. Technique parameters included kVp pair (denoted [kVp(L)/kVp(H)]) and dose allocation (the proportion of dose in low- and high-energy projections), each optimized to provide maximum signal difference-to-noise ratio in DE images. Experiments involved a chest phantom representing an average patient size and containing simulated ribs and lung nodules. Low- and high-energy kVp were varied from 60-90 and 120-150 kVp, respectively. The optimal kVp pair was determined to be [60/130] kVp, with image quality showing a strong dependence on low-kVp selection. Optimal dose allocation was approximately 0.5-i.e., an equal dose imparted by the low- and high-energy projections. The results complement earlier studies of optimal DE soft-tissue image acquisition, with differences attributed to the specific imaging task. Together, the results help to guide the development and implementation of high-performance DE imaging systems, with applications including lung nodule detection and diagnosis, pneumothorax identification, and musculoskeletal imaging (e.g., discrimination of rib fractures from metastasis).
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".