Energy subtraction angiography is comparable to digital subtraction angiography in terms of iodine Rose SNR
Bibliographic record
Abstract
PURPOSE: X-ray digital subtraction angiography (DSA) is widely used for vascular imaging. However, motion artifacts render it largely unsuccessful for some applications including cardiac imaging. Dual-energy imaging using fast kV switching was proposed in the past to provide the benefits of DSA with fewer motion artifacts, but image quality was inferior to DSA. This study compares the iodine Rose SNR that can be achieved using dual-energy methods, called energy-subtraction angiography (ESA), with that of DSA and examines the technical conditions required to achieve near-optimal SNR. METHODS: A Rose SNR model is described, experimentally validated, and used to compare ESA with DSA. The model considers detector quantum efficiency, readout noise (quantum-limit exposure), and scatter-to-primary ratio. RESULTS: The theoretical Rose SNR showed excellent agreement with experimental results for both ESA and DSA images, and shows that near-optimal SNR is harder to achieve with ESA than DSA. In comparison to DSA, ESA requires: (1) high detector quantum efficiency at a higher energy (120 kV); (2) lower detector readout noise by a factor of four (approximately 0.005 μGy air KERMA or lower); and (3) lower scatter-to-primary ratio by a factor of three (approximately 0.05 or lower). These conditions were not achievable in the past, and remain difficult but not impossible to achieve at present. CONCLUSIONS: ESA and DSA can provide similar iodine Rose SNR for the same patient exposure, but only when satisfying the above conditions. This may explain why dual-energy methods have been unsuccessful in the past and suggests ESA methods may offer a viable alternative to DSA when implemented under optimal conditions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".