Noise in magnitude magnetic resonance images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it