Minimizing dose during fluoroscopic tracking through geometric performance feedback
Bibliographic record
Abstract
PURPOSE: There is a growing concern regarding the dose delivered during x-ray fluoroscopy guided procedures, particularly in interventional cardiology and neuroradiology, and in real-time tumor tracking radiotherapy and radiosurgery. Many of these procedures involve long treatment times, and as such, there is cause for concern regarding the dose delivered and the associated radiation related risks. An insufficient dose, however, may convey less geometric information, which may lead to inaccuracy and imprecision in intervention placement. The purpose of this study is to investigate a method for achieving the required tracking uncertainty for a given interventional procedure using minimal dose. METHODS: A simple model is used to demonstrate that a relationship exists between imaging dose and tracking uncertainty. A feedback framework is introduced that exploits this relationship to modulate the tube current (and hence the dose) in order to maintain the required uncertainty for a given interventional procedure. This framework is evaluated in the context of a fiducial tracking problem associated with image-guided radiotherapy in the lung. A particle filter algorithm is used to robustly track the fiducial as it traverses through regions of high and low quantum noise. Published motion models are incorporated in a tracking test suite to evaluate the dose-localization performance trade-offs. RESULTS: It is shown that using this framework, the entrance surface exposure can be reduced by up to 28.6% when feedback is employed to operate at a geometric tracking uncertainty of 0.3 mm. CONCLUSIONS: The analysis reveals a potentially powerful technique for dynamic optimization of fluoroscopic imaging parameters to control the applied dose by exploiting the trade-off between tracking uncertainty and x-ray exposure per frame.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".