Correction of partial volume effect in the projections in PET studies
Bibliographic record
Abstract
Partial volume effect (PVE) in PET generates under-estimation of radiotracer concentration in small size structures. Consequently, the image contrast is qualitatively degraded and, quantitatively, the reduced signal intensity leads to erroneous physiological parameter estimation. The most popular approach to correct for PVE is based on recovery factors (RF). RFs are first estimated from objects of known sizes, then they are used to amplify the intensity of small structures in the PET images whose dimensions are usually determined with CT or MRI. Other approaches use deconvolution of the scanner response function in the images. In all of these methods, PVE is globally corrected at the image level. Since PVE results from inaccurate measurements of the PET projection data, we hypothesize that PVE can be more accurately corrected directly on the projection. In this paper, the projections from a phantom with eight cylinders of various diameters all filled with the same radiotracer concentration were measured with PET and also simulated free of PVE. A corresponding set of deconvolution kernel functions were obtained from these two sets of data and applied directly on projections to correct for PVE. The method was applied in phantoms and in rat heart and tumor studies. Based on the widths of objects in each of the projections of the rat and phantom measurements with respect to those of the cylinders, the appropriate kernels were used to generate PVE-corrected projections by deconvolution from which the PVE corrected images were reconstructed. For irregular objects such as the non-circular shaped tumor and the myocardium, several different kernels were used to correct for the variable PVE as a function of projection angles and the image with the properly restored intensity was reconstructed with lesser amplification of the noise as if we apply RF directly on the images. In conclusion, the deconvolution of the measured projections with individual kernels corresponding to the degradation along each projection allowed to more accurately correcting for PVE.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".