Effect of black blood MR image quality on vessel wall segmentation
Bibliographic record
Abstract
Black blood MRI has become a popular technique for measuring arterial wall area as an indicator of plaque size. Computer-assisted techniques for segmenting vessel boundaries have been developed to increase measurement precision. In this study, the carotid arteries of four normal subjects were imaged at seven different fields of view (FOVs), keeping all other imaging parameters fixed, to determine whether spatial resolution could be increased at the expense of image quality without sacrificing precision. Wall areas were measured via computer-assisted segmentation of the vessel boundaries performed repeatedly by two operators. Analysis of variance (ANOVA) demonstrated that the variability of wall area measurements was below 1.5 mm(2) for in-plane spatial resolutions between 0.22 mm and 0.37 mm. An inverse relationship between operator variability and the signal difference-to-noise ratio (SDNR) demonstrated that semi-automatic segmentation of the wall boundaries was robust for SDNR >3, defining a criterion above which subjective image quality can be degraded without an appreciable loss of information content. Our study also suggested that spatial resolutions higher than 0.3 mm may be required to quantify normal wall areas to within 10% accuracy, but that the reduced SNR associated with the higher resolution may be tolerated by semi-automated wall segmentation without an appreciable loss of precision.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".