A search for optimal x-ray spectra in iodine contrast media mammography
Bibliographic record
Abstract
The aim of this work was to search for the optimal x-ray tube voltage and anode-filter combination in digital iodine contrast media mammography. In the optimization, two entities were of interest: the average glandular dose, AGD, and the signal-to-noise ratio, SNR, for detection of diluted iodine contrast medium. The optimum is defined as the technique maximizing the figure of merit, SNR2/AGD. A Monte Carlo computer program was used which simulates the transport of photons from the x-ray tube through the compression plate, breast, breast support plate, anti-scatter grid and image detector. It computes the AGD and the SNR of an iodine detail inside the compressed breast. The breast thickness was varied between 2 and 8 cm with 10-90% glandularity. The tube voltage was varied between 20 and 55 kV for each anode material (Rh, Mo and W) in combination with either 25 microm Rh or 0.05-0.5 mm Cu added filtration. The x-ray spectra were calculated with MCNP4C (Monte Carlo N-Particle Transport Code System, version 4C). A CsI scintillator was used as the image detector. The results for Rh/0.3 mmCu, Mo/0.3 mmCu and W/0.3 mmCu were similar. For all breast thicknesses, a maximum in the figure of merit was found at approximately 45 kV for the Rh/Cu, Mo/Cu and W/Cu combinations. The corresponding results for the Rh/Rh combination gave a figure of merit that was typically lower and more slowly varying with tube voltage. For a 4 cm breast at 45 kV, the SNR2/AGD was 3.5 times higher for the Rh/0.3 mmCu combination compared with the Rh/Rh combination. The difference is even larger for thicker breasts. The SNR2/AGD increases slowly with increasing Cu-filter thickness. We conclude that tube voltages between 41 and 55 kV and added Cu-filtration will result in significant dose advantage in digital iodine contrast media mammography compared to using the Rh/Rh anode/filter combination at 25-32 kV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".