Polyenergetic CT sinogram generator
Bibliographic record
Abstract
Energy-sensitive X-ray detection devices operating in photon counting mode are getting growing interest since the last decade. By offering a promise of lower dosage requirements and spectroscopic analysis capabilities, they might redefine the paradigm of clinical X-ray measurements in a near future. A simulation software reproducing the data collection scheme of such detection devices was implemented. Without trying to reproduce the in-depth electronic mechanism of those devices, it is rather oriented toward the overall quality of the measured data in terms of simple detection characteristics. Build upon rugged and proven software components, the generator includes realistic material definitions with respect to energy-dependent attenuation. Projections are measured and features such as energy resolution, number of detected energy levels, counting noise statistics and data weighting schemes are taken into account. Using the proposed method, iterative image reconstructions show that classic beam-hardening related artefacts can successfully be reproduced. The proposed method is intended to be used as a tool aimed at predicting the imaging capabilities of these forthcoming energy-sensitive detection devices and to help in the design of their specifications. Being an easily parameterizable analytical tool, it will also be useful to validate the behavior of new dedicated polyenergetic reconstruction algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".