SU‐E‐J‐43: Autotuning Imaging Parameters in X‐Ray Fluoroscopic Tracking for Dose Savings
Bibliographic record
Abstract
PURPOSE: In fluoroscopy-guided clinical procedures that involve tracking objects over long treatment times, there is a need for reducing the amount of imaging dose delivered to the patient and the operating staff. In this study, we introduce a feedback metric to minimize tube current while maintaining a targeting precision threshold. METHOD AND MATERIALS: An acrylic sphere (1.6mm in diameter) was imaged at tube currents ranging from 0.5 mA to 0.9mA (1s) at a fixed energy of 50kVp. The images were acquired on a Varian Paxscan 4030A (2048×1536 pixels, 1100 mm source-to-axis distance, 1570 mm source-to-detector distance). A state feedback metric (tr(C)) based on the current object position was computed and plotted as afunction of the tube current. Next, the sphere was tracked using a particle filter with a bowtie filter (4.3-764mm thickness, Al) in the background. The tr(C) was used a by a PID controller to modulate the tube current in order to maintain a specified precision as the sphere traversed regions of varying thickness corresponding to the bowtie filter. RESULT: . CONCLUSION: This work presents a relation between tr(C) and thetube current which can be leveraged to reduce imaging dose to patients and staff.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".