Use of a digitally reconstructed radiograph-based computer simulation for the optimisation of chest radiographic techniques for computed radiography imaging systems
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to derive an optimum radiographic technique for computed radiography (CR) chest imaging using a digitally reconstructed radiograph computer simulator. The simulator is capable of producing CR chest radiographs of adults with various tube potentials, receptor doses and scatter rejection. METHODS: Four experienced image evaluators graded images of average and obese adult patients at different potentials (average-sized, n=50; obese, n=20), receptor doses (n=10) and scatter rejection techniques (average-sized, n=20; obese, n=20). The quality of the images was evaluated using visually graded analysis. The influence of rib contrast was also assessed. RESULTS: For average-sized patients, image quality improved when tube potential was reduced compared with the reference (102 kVp). No scatter rejection was indicated. For obese patients, it has been shown that an antiscatter grid is indicated, and should be used in conjunction with as low a tube potential as possible (while allowing exposure times <20 ms). It is also possible to reduce receptor air kerma by 50% without adversely influencing image quality. Rib contrast did not interfere at any tube potential. CONCLUSIONS: A virtual clinical trial has been performed with simulated chest CR images. Results indicate that low tube potentials (<102 kVp) are optimal for average and obese adults, the former acquired without scatter rejection, the latter with an anti-scatter grid. Lower receptor (and therefore patient doses) than those used clinically are possible while maintaining adequate image quality.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".