Radiation Dosimetry of 3D Rotational Neuroangiography and 2D-DSA in Children
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The benefit-risk assessment concerning radiation use in pediatric neuroangiography requires an extensive understanding of the doses delivered. This work evaluated the effective dose of 3D rotational angiography in a cohort of pediatric patients with complex neurovascular lesions and directly compared it with conventional 2D-biplane DSA. MATERIALS AND METHODS: Thirty-three 3D rotational angiography acquisitions were acquired in 24 pediatric patients (mean age, 10.4 years). When clinically indicated, following 2D-biplane DSA, 3D rotational angiography was performed with 1 of 3 technical protocols (2 subtracted, 1 unsubtracted). The protocols consisted of 1 factory and 2 customized techniques, with images subsequently reconstructed into CT volumes for clinical management. Raw projections and quantitative dose metrics were evaluated, and the effective dose was calculated. RESULTS: All 3D rotational angiography acquisitions were of diagnostic quality and assisted in patient management. The mean effective doses were 0.5, 0.12, and 0.06 mSv for the factory-subtracted, customized-subtracted, and customized-unsubtracted protocols, respectively. The mean effective dose for 2D-biplane DSA was 0.9 mSv. A direct intraprocedural comparison between 3D and 2D acquisitions indicated that customized 3D rotational angiography protocols delivered mean relative doses of 9% and 15% in unsubtracted and subtracted acquisitions, respectively, compared with biplane DSA, whereas the factory subtracted protocol delivered 68%. CONCLUSIONS: In pediatric neuroangiography, the effective dose for 3D rotational angiography can be significantly lower than for 2D-biplane DSA and can be an essential adjunct in the evaluation of neurovascular lesions. Additionally, available 3D rotational angiography protocols have significant room to be tailored for effectiveness and dose optimization, depending on the clinical question.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".