Characterization of PET partial volume corrections for variable myocardial wall thicknesses
Bibliographic record
Abstract
Limited scanner resolution and cardiac motion contribute to partial volume (PV) averaging of positron emission tomography (PET) images. An extravascular (EV) density image, created from the subtraction of a blood pool (BP) from a transmission (TX) image can be used to estimate PV losses in the myocardium (MYO). A phantom emulating the left ventricle myocardium, with a variable wall thickness (5 mm to 25 mm), was used to characterize the method for use in 3-D PET /sup 18/FDG studies. At a myocardial thickness of 5 mm, 40% recovery of the activity was obtained. At myocardial thicknesses greater than 20 mm, full recovery was seen. Prior to EV image creation, the morphological operators dilation/erosion were applied to the BP and TX images to account for the presence of the phantom's plastic walls, which would otherwise bias the EV values. Dividing MYO by the EV values improved the recovery to 95% at 5 mm, however, TX ring artifacts and the anisotropic nature of dilation/erosion contributed to errors in the EV image. Instead of using dilation/erosion, a second method, involving placement of adjusted ROIs on the BP and TX images was investigated. Use of these new EV values, as well as placement of the cardiac phantom in a chest phantom to reduce TX ring artifacts, allowed for 90% recovery of the activity at 5 mm. These results show that the EV density image can correct for PV averaging with 3-D PET over a range of myocardial thicknesses applicable to patient studies. However, in the thinnest regions, the method was found to be sensitive to errors in both the blood pool and transmission images.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".