Coupling of CT and PET-18F-FDG Imaging in Arteries with Calcification
Bibliographic record
Abstract
PET imaging of arteries is drastically dominated by the emission of activity from blood. In addition, the artery wall (2.3 mm) is thin in comparison to the whole artery section and is thus subject to partial volume effect (PVE). In fact, the whole artery area which has a diameter of 38 mm for the abdominal aorta is affected by PVE. Currently, 18F-FDG uptake in the artery is evaluated by means of standard uptake value (SUV) and tissue-to-blood ratio (TBR). In the case of TBR, the activity in blood is defined from an image of a vein. In the present work, the dynamic 18F-FDG images were decomposed with factor analysis (FA) in images of blood and tissue. Artery images were corrected for PVE with recovery factors deduced from a phantom. Eight subjects were imaged with CT and PET in dynamic mode. Five subjects were under medication for atherosclerosis. The tissue and blood images were used for SUV and TBR calculation and compared to the usual values obtained from the measured images. SUV and TBR were classified based on five levels of intensity of the artery calcifications on CT images and on the extent of the calcifications. SUV and TBR extracted from the decomposed images provided more accurate values than those deduced from the measured images. The method can be used in staging of atherosclerosis disease in elderly and it can be useful in the clinic with imaging in reduced time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".