Coupling of 18F-NaF and 18F-FDG PET/CT Dynamic Imaging for the Detection of Arterial Inflammation
Bibliographic record
Abstract
Arterial inflammation is an indicator of atheromatous plaque vulnerability to detach and to obstruct blood vessels in the brain thus causing vascular complications. To date, it is difficult to predict the plaque vulnerability. In the present study we report plaque metabolism from images acquired in 18 volunteers aged 65 to 85 years imaged with computed tomography (CT) and positron emission tomography (PET) with 18F-sodium fluoride (18F-NaF) and 18F-fluorodeoxyglucose (18F-FDG). Regions of interest (ROIs) were drawn on the artery segments including blood by means of active contour technique on both CT and PET images. The arteries were considered calcified if they contained 2 or more adjacent pixels of density above 130 Hounsfield Units (HU). The arteries on PET images were corrected for partial volume effect and the radiotracer uptake was quantified with the Standard uptake value (SUV). A total of 1338 arterial segments were analyzed, 766 were non-calcified and 572 had calcifications. The calcification in an artery segment was found as a single or multiple patterns. For 18F-NaF, the mean SUV value was 1.6840 ± 0.3220 for non-calcified segments and 1.8800 ± 0.3651 for calcified segments P<0.05, and for 18F-FDG, 2.0040 ± 0.3804 for non-calcified and 2.0913 ± 0.3722 for calcified segments P<0.05. Clustering CT non-calcified segments based on arterial wall density, excluding blood, resulted in two clusters C1 and C2 with a mean density of 30.63 ± 5.06 HU in C1 and 43.06 ± 4.71 HU in C2. The evaluation of 18F-NaF SUV in C1 was found 1.6252 ± 0.3308, and in C2 it was 1.7475 ± 0.2963, and similarly for 18F-FDG, SUV was 1.9307 ± 0.3394 in C1 and 2.0544 ± 0.3882 in C2. In the three imaging types, the difference between C1 and C2 was found statistically significantly different confirming the correlation of the high arterial wall density on CT images, in the absence of calcification, with an active micro-calcification (high 18F-NaF SUV) and inflammation (high 18F-FDG SUV). Based on these results, it is suggested to estimate the arterial inflammation with CT imaging only.
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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 | 0.001 | 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 | 0.001 |
| 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".