Impact of T2 decay on carotid artery wall thickness measurements
Bibliographic record
Abstract
PURPOSE: To investigate the impact of T2 relaxation of the carotid wall on measurements of its thickness. MATERIALS AND METHODS: The common carotid artery wall was imaged using a spin echo sequence acquired at four echo times (17 ms to 68 ms) in 65 participants as part of VALIDATE study. Images were acquired transverse to the artery 1.5 cm proximal to the flow divider. Mean wall thickness, mean wall signal intensity, lumen area, and outer wall area were measured for each echo. Contours were also traced on the image from the fourth echo and then propagated to the images from the preceding echoes. This was repeated using the image from the first echo. Mean wall signal intensity measurements at the four echo times were fit to a mono-exponential decay curve to derive the mean T2 relaxation time for each set of contours. RESULTS: Mean wall thickness decreased with increasing echo time, with an average thickness reduction of 8.6% between images acquired at the first and last echo times (TE) (0.93 mm at TE 17 ms versus 0.85 mm at TE 68 ms, P < 0.001). Average T2 relaxation time of the carotid wall decreased by 3% when the smaller contours from the last echo were used, which excluded the outer-most layer (54.3 ± 7.6 ms versus 52.7 ± 6.6 ms, P = 0.03). CONCLUSION: Carotid wall thickness measurements decrease with echo time as expected by the fast T2 relaxation time of the outer-most layer, namely the adventitia. A short echo time is needed for thickness measurements to include adventitia, which plays an important role in plaque development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".