Noise in magnitude magnetic resonance images
Bibliographic record
Abstract
Abstract The aims of this article are to review the properties of noise in magnitude MR images to clarify the terminology used when referring to the noise and to discourage the use of the terms Rician noise and Rician noise bias . The distribution of measured MR pixel intensities in the presence of noise is known to be Rician, and the width of this distribution is directly related to the Gaussian noise on the measured real and imaginary signals. It is the pixel magnitude values that follow the Rician distribution, not the noise. The term Rician noise should be used cautiously or, better still, avoided completely since inherent to this terminology is behavior that is not normally associated with noise, such as dependence on signal strength. This terminology is misleading and can lead to conceptual and practical misunderstandings. It is better to relate the image noise to the Gaussian noise on the real and imaginary signals. © 2008 Wiley Periodicals, Inc.Concepts Magn Reson Part A 32A:409–416, 2008.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".