Noise power properties of a cone‐beam CT system for breast cancer detection
Bibliographic record
Abstract
The noise power properties of a cone-beam computed tomography (CT) system dedicated for breast cancer detection were investigated. Uniform polyethylene cylinders of various diameters were scanned under different system acquisition conditions. Noise power spectra were calculated from difference data generated by subtraction between two identical scans. Multidimensional noise power spectra (NPS) were used as the metric to evaluate the noise properties of the breast CT (bCT) under different system acquisition and reconstruction conditions. A comprehensive investigation of the noise properties was performed in regard to system acquisition parameters including kVp, mA, number of cone-beam projection images used, cone angle, and object size. The influence on reconstruction parameters including interpolation method, reconstruction filter, field of view, matrix size, and slice thickness were also studied. Under certain conditions, the zero-dimensional NPS (image variance) was used as a quantitative index to compare the influence from different scan parameters, especially the radiation dose. If the total scan dose is changed by linearly changing the total number of projection images while the dose per frame is kept constant, the noise power has a linear relationship with the reciprocal of the total dose. If the total scan dose is changed by linearly changing the dose per frame while the total number of projection images is kept constant, the noise power has a quadratic relationship with the reciprocal of the total dose. With the same amount of total dose, using fewer projection images results in lower image noise power in the CT image. Quantitative results from this noise power analysis provide guidance for the bCT system operation, optimization, and data reconstruction.
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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 | 0.000 |
| 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".