The potential role of IDEAL MRI for identification of lipids and hemorrhage in carotid artery plaques
Bibliographic record
Abstract
Hemorrhage and lipid deposits contribute to instability in atherosclerotic plaques. Unstable carotid artery plaques can lead to cerebral ischemic events. While MRI studies have shown the ability to identify plaque components, the identification of hemorrhage and lipids has proven to be problematic. The purpose of this study was to quantitatively evaluate the potential of the MRI fat/water separation method known as iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) to complement and improve existing methods for the identification of hemorrhage and lipids in carotid artery plaques. Fifteen asymptomatic subjects with 50-79% stenosis of at least one carotid artery were enrolled. Hemorrhage and lipid components within carotid plaques were identified using previously published criteria based on the multiple contrast-weighted (MCW) method (3D Time-of-Flight (3D-TOF), T1-Weighted (T1W) and T2-Weighted (T2W)). The hemorrhage:muscle, lipid:muscle and intra-plaque lipid:hemorrhage signal intensity ratios (SIR) and contrast to noise ratios (CNR) were measured on MCW and compared to IDEAL black-blood images. No differences were found between any of the MCW methods for any of the SIRs measured. The IDEAL Fat images had higher lipid:muscle and lipid/hemorrhage SIRs (p<0.001) compared to IDEAL Water and all MCW image sequence types. The mean values of IDEAL Fat hemorrhage:muscle SIR and CNR were nearly unity (1.1±0.6) and nearly zero (0.1±1.1), respectively. The IDEAL Water imaging was not significantly different than any of the MCW methods for any of the SIRs or for the hemorrhage:muscle CNR of 3D-TOF, while its CNRs were significantly higher than IDEAL Fat lipid:muscle (p<0.05) and lipid:hemorrhage (p<0.001) and all MCW methods (p<0.001). The addition of IDEAL Water and Fat imaging to the MCW method shows potential to improve the identification of hemorrhage and lipid structures in carotid artery plaques.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".