Parallel cascades: New ways to describe noise transfer in medical imaging systems
Bibliographic record
Abstract
A generalized approach to describing transfer of the noise power spectrum through medical imaging systems has been developed over the past several years in which image-forming processes are represented in terms of a cascade of amplified point processes. Until recently, this approach has been restricted to serial cascades only. Here we develop a generalized expression for the cross covariance of amplified point processes and an expression for the cross spectral density for wide-sense stationary conditions. These results extend the generalized transfer-theory approach to include the description of more complex image-forming processes involving parallel cascades of quantum amplification processes. This parallel-cascade approach is used to develop a theoretical expression for noise-power transfer in a simple radiographic screen that includes the effect of characteristic x-ray reabsorption. The result confirms earlier work by Metz and Vyborny, who showed that reabsorption increases image noise and decreases the detective quantum efficiency at low spatial frequencies. Use of the transfer-theory approach facilitates a straightforward generalization to many new digital imaging systems including conventional angiographic and active-matrix flat-panel systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".