Fourier Analysis of Noise Characteristics in Cone-Beam Microtomography Laboratory Scanners
Bibliographic record
Abstract
GOAL: We investigate the signal and noise performance of an x-ray microtomography system that incorporates a complementary metal-oxide-semiconductor flat-panel detector as a projection image receptor. METHODS: Signal and noise performance is analyzed in the Fourier domain using modulation-transfer function (MTF), noise-power spectrum (NPS), and noise-equivalent number of quanta (NEQ) with respect to magnification and different convolution kernels for image reconstruction. RESULTS: Higher magnification provides lower NPS, and thus, higher NEQ performance in the transaxial planes from microtomography. A window function capable of smoothing the ramp filter edge to below one-half of the Nyquist limit results in better performance in terms of NPS and NEQ. The characteristics of convolution kernels do not affect signal and noise performance in longitudinal planes; hence, MTF performance mainly dominates the NEQ performance. The signal and noise performances investigated in this study are demonstrated with images obtained from the contrast phantom and postmortem mouse. CONCLUSION: The results of our study could be helpful in developing x-ray microtomography systems based on flat-panel detectors.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".