<title>Experimental and theoretical studies of dual energy subtraction angiography (DESA) performed using laser-based x-ray source</title>
Bibliographic record
Abstract
Two types of x-ray sources for dual energy subtraction angiography (DESA), laser-based and conventional, were investigated. A Tabletop Terawatt laser was used to create x-ray source with Ba, La, Nd, Gd, and Ce targets. A theoretical model of image quality was developed. A Figure of Merit, FOM equals SNR./(integral dose)1/2, was obtained. Images of an angiographic contrast detail phantom were obtained using laser-driven x-ray source in DESA regime and a standard angiography unit in DSA regime. The log-signals due to Iodine contrast agent in the images were measured and compared with the theoretical model predictions. The integral dose was estimated. We found that the La and Ba lines extracted by a monochromator are optimal for imaging Iodine contrast with laser-based DESA. In this case, SNR exhibits three- to five-fold improvement, as compared to SNR expected for a tube-based DESA system. Consequently, dose utilization, as defined by FOM, improves by factor of two to three, depending on patient thickness and scatter conditions. When only filters are used, SNR and FOM due to laser-based system are comparable to those due to tube-based DESA. In this case, preferable target/filter combination for the laser system is Ba/I and Ce/Nd for the low- and high-beam, respectively.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".