Correction of partial volume effects for PET imaging: a comparison study
Bibliographic record
Abstract
The low spatial resolution of PET scanners results in partial volume (PV) effects limiting the quantification in small structures. In this study, we compare the correction algorithms implemented at the Research Center Juelich (PVC-J) and at the Brain Imaging Centre of Montreal (PVC-M). PVC-J algorithm: The corrected grey matter (GM) activity image is obtained by dividing voxel-wise the uncorrected GM image by the GM probability map, derived from the convolution of the corresponding MR segmented image by a 3D spatially variant gaussian function, which reproduces the actual PET image resolution. PVC-M algorithm: It accounts for the mutual PV effects between any tissue structure. The cross-contamination factors are computed for each of the structures yielding the geometric transfer matrix which is solved to get the true mean activity values. A PET dynamic acquisition of an adenosin receptor study was simulated using the Zubal's computerized phantom and the SORTEO Monte Carlo PET simulator. A global spatial resolution of 9.5mm was used with both methods. Mean deviations over the dynamic data from the reference values are ranging from +6% for the GM region and PVC-J to -10% for globus pallidus and PVC-M. The data show a very high consistency of the results obtained from the two different methods concerning the adenosine receptor study taken as basis for the simulation.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".