Bibliographic record
Abstract
Dietrich et al (1) present an interesting comparison of different approaches in the estimation of signal-to-noise ratio (SNR) in MRI images and investigate the influence of the use of multichannel coils, parallel imaging, and reconstruction filters. One conclusion of the article is that the widely used SNR estimation method using a background region of interest (ROI) for the estimation of the signal standard deviation provides inaccurate results when multichannel coils are employed. For an eight-channel coil, such as the one employed in Ref.1, the factor between the noise mean in a background ROI (mair) and the noise standard deviation (σmean) obtained with Eq. [3] is 0.254 and is significantly different from the value of 0.797 obtained with Eq. [1]. This important difference probably explains why the ratios SNR(8CH)/SNR(1CH) presented in Table 1 and 2 of Ref.1 are significantly lower for SNRmean when compared to the values obtained with the other methods. The values of SNRstdv for the same ratios are in better agreement with the values obtained by using the other methods because Eqs. [2] and [4] give factors that are more similar (1.526 in comparison to 1.426). The evaluation of the signal standard deviation from a background ROI definitely appears risky when parallel imaging and reconstruction filters are employed or when the correlation between the channels is high. However, if correlation is low and as long as a standard sum-of-squares reconstruction without parallel imaging is employed, the use of the good factors should definitely improve the accuracy of the SNR estimation when multichannel coils are used, especially when coils with different number of channels are compared. Guillaume Gilbert BSc*, * Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".