Imaging the vessel wall in major peripheral arteries using susceptibility‐weighted imaging
Bibliographic record
Abstract
PURPOSE: To demonstrate a novel contrast mechanism for imaging the vessel wall and vessel wall calcification using susceptibility-weighted imaging (SWI). MATERIALS AND METHODS: Eighteen subjects were imaged with multidetector computed tomography (MDCT) and high-resolution SWI at 3T. The SWI imaging parameters were optimized to allow for the best visualization of the femoral artery lumen and the arterial wall in magnitude and phase images, respectively. SWI-filtered phase data were used to evaluate the diamagnetic susceptibility of vessel wall and of putative vessel wall calcification. Imaging was performed using TE = 15.6 msec (in-phase for fat); TR = 25 msec, flip angle (FA) = 10 degrees , bandwidth (BW) = 80 Hz/pixel, resolution = 0.5 x 0.5 mm in-plane and 1.0 mm through-plane, an acquisition matrix of 512 x 384 x 64 (for read, phase, and slice-select directions), and a total scan time of 8 minutes. RESULTS: Nineteen calcifications were identified in CT and SWI and they correlated well in both size and position. The contrast-to-noise ratio between the blood signal in the lumen of the artery and arterial wall was 11.7:1 and 7.4:1 in magnitude and in phase images, respectively. CONCLUSION: SWI provides a novel means to visualize vessel wall and recognize the presence of calcification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".