VALIDATION OF A NOVEL TECHNIQUE FOR CREATING SIMULATED RADIOGRAPHS USING COMPUTED TOMOGRAPHY DATASETS
Bibliographic record
Abstract
Understanding radiographic anatomy and the effects of varying patient and radiographic tube positioning on image quality can be a challenge for students. The purposes of this study were to develop and validate a novel technique for creating simulated radiographs using computed tomography (CT) datasets. A DICOM viewer (ORS Visual) plug-in was developed with the ability to move and deform cuboidal volumetric CT datasets, and to produce images simulating the effects of tube-patient-detector distance and angulation. Computed tomographic datasets were acquired from two dogs, one cat, and one horse. Simulated radiographs of different body parts (n = 9) were produced using different angles to mimic conventional projections, before actual digital radiographs were obtained using the same projections. These studies (n = 18) were then submitted to 10 board-certified radiologists who were asked to score visualization of anatomical landmarks, depiction of patient positioning, realism of distortion/magnification, and image quality. No significant differences between simulated and actual radiographs were found for anatomic structure visualization and patient positioning in the majority of body parts. For the assessment of radiographic realism, no significant differences were found between simulated and digital radiographs for canine pelvis, equine tarsus, and feline abdomen body parts. Overall, image quality and contrast resolution of simulated radiographs were considered satisfactory. Findings from the current study indicated that radiographs simulated using this new technique are comparable to actual digital radiographs. Further studies are needed to apply this technique in developing interactive tools for teaching radiographic anatomy and the effects of varying patient and tube positioning.
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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 | 0.005 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".