TH‐C‐332‐03: Noise Power Properties of a Cone‐Beam CT System for Breast Cancer Detection
Bibliographic record
Abstract
Purpose: To investigate the noise power properties of a cone‐beam breast CT (bCT) system. Method and Materials: Polyethylene cylinders were scanned under different acquisition conditions. Normalized noise power spectra (NNPS) were calculated from difference images by subtracting two identical scans. Multi‐dimensional NNPS were used to evaluate the noise properties of the bCT under different acquisition and reconstruction parameters including kVp, mA, number of projections, cone angle, object size, interpolation method, reconstruction filter, field of view (FOV), matrix size, and slice thickness. Results: Findings from the analysis: The bCT NNPS is rotational symmetric within the coronal plane and its shape is determined by the interpolation method and filter. For a cone angle range from 0° to 14°, the shape of the NPS curve changes slightly. The image variance increases slightly with increasing cone angle. Noise aliasing can be avoided if the sampling frequency of CT image is beyond a specific threshold. The coronal plane noise power decreases with increasing slice thickness. If the radiation dose (mA) is below a specific threshold, the electronic noise from the detector plays a dominate role and the system is no longer quantum limited. The threshold value decreases with increasing object size. When the radiation dose is beyond this threshold, the system is quantum limited. The image variance has a power law relationship of approximately −1.1 with the total dose. If the dose is increased by linearly increasing the number of projections but not the tube current, the noise power has an inverse linear relationship with the dose. When the dose level is kept constant, the image noise also depends on the x‐ray spectrum and the object size. Conclusion: A thorough investigation of the noise power was performed. Quantitative results provide guidance for the bCT system operation, optimization and data reconstruction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".