Order of magnitude reduction of fluoroscopic x-ray dose
Bibliographic record
Abstract
The role of fluoroscopic imaging is critical for diagnostic and image guided therapy. However, fluoroscopic imaging can require significant radiation leading to increased cancer risk and non-stochastic effects such as radiation burns. Our purpose is to reduce the exposure and dose to the patient by an order of magnitude in these procedures by use of the region of interest method. Method and Materials: Region of interest fluoroscopy (ROIF) uses a partial attenuator. The central region of the image has full exposure while the image periphery, there to provide context only, has a reduced exposure rate. ROIF using a static partial attenuator has been shown in our previous studies to reduce the dose area product (DAP) to the patient by at least 2.5 times. Significantly greater reductions in DAP would require improvements in flat panel detectors performance at low x-ray exposures or a different x-ray attenuation strategy. Thus we have investigated a second, dynamic, approach. We have constructed an x-ray shutter system allowing a normal x-ray exposure in the region of interest while reducing the number of x-ray exposures in the periphery through the rapid introduction, positioning and removal of an x-ray attenuating shutter to block radiation only for selected frames. This dynamic approach eliminates the DQE(0) loss associated with the use of static partial attenuator applied to every frame thus permitting a greater reduction in DAP. Results: We have compared the two methods by modeling and determined their fundamental limits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".