Phantom Studies Investigating Extravascular Density Imaging for Partial Volume Correction of 3-D PET<tex>$^18$</tex>FDG Studies
Bibliographic record
Abstract
Limited scanner resolution and cardiac motion contribute to partial volume (PV) averaging of cardiac PET images. An extravascular (EV) density image, created from the subtraction of a blood pool scan from a transmission image, has been used to correct for PV averaging in H/sub 2//sup 15/O studies using 2-D imaging but not with 3-D imaging of other tracers such as /sup 18/FDG. A cardiac phantom emulating the left ventricle was used to characterize the method for use in 3-D PET studies. Measurement of the average myocardial activity showed PV losses of 32% below the true activity (p<0.001). Initial application of the EV density correction still yielded a myocardial activity 13% below the true value (p<0.001). This failure of the EV density image was due to the 1.66 mm thick plastic barrier separating the myocardial and ventricular chambers within the phantom. Upon removal of this artifact by morphological dilation of the blood pool, the corrected myocardial value was within 2% of the true value (p=ns). Spherical ROIs (diameter of 2 to 10 mm), evenly distributed about the myocardium, were also used to calculate the average activity. The EV density image was able to account for PV averaging throughout the range of diameters to within a 5% accuracy, however, a small bias was seen as the size of the ROIs increased. This indicated a slight mismatch between the emission and transmission image resolutions, a result of the difference in data acquisitions (i.e., span and ring difference) and default smoothing. These results show that the use of EV density image to correct for PV averaging is possible with 3-D PET. A method of correcting barrier effects in phantoms has been presented, as well as a process for evaluating resolution mismatch.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".