Noise aliasing in interline‐video‐based fluoroscopy systems
Bibliographic record
Abstract
Video-based imaging systems for continuous (nonpulsed) x-ray fluoroscopy use a variety of video formats. Conventional video-camera systems may operate in either interlaced or progressive-scan modes, and CCD systems may operate in interline- or frame-transfer modes. A theoretical model of the image noise power spectrum corresponding to these formats is described. It is shown that with respect to frame-transfer or progressive-readout modes, interline or interlaced cameras operating in a frame-integration mode will result in a spectral shift of 25% of the total image noise power from low spatial frequencies to high. In a field-integration mode, noise power is doubled with most of the increase occurring at high spatial frequencies. The differences are due primarily to the effect of noise aliasing. In interline or interlaced formats, alternate lines are obtained with each video field resulting in a vertical sampling frequency for noise that is one half of the physical sampling frequency. The extent of noise aliasing is modified by differences in the statistical correlations between video fields in the different modes. The theoretical model is validated with experiments using an x-ray image intensifier and CCD-camera system. It is shown that different video modes affect the shape of the noise-power spectrum and therefore the detective quantum efficiency. While the effect on observer performance is not addressed, it is concluded that in order to minimize image noise at the critical mid-to-high spatial frequencies for a specified x-ray exposure, fluoroscopic systems should use only frame-transfer (CCD camera) or progressive-scan (conventional video) formats.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".