Two methods to generate templates for template-based partial volume effect correction: SPECT phantom experiments
Bibliographic record
Abstract
In this paper, we explore the applicability of template-based compensation for the partial volume effect (PVE) for situations where (i) the image has multiple uptake sites (tumors and organs) but only one of them is treated as a region of interest (ROI) with the boundaries available from a high-resolution modality and (ii) no information regarding activities inside or outside this ROI is a priori available. We modeled this situation by performing SPECT acquisitions of phantoms containing 21 containers, which had different shapes and sizes and were filled with different levels of activity. In our analysis, each of these containers was treated as an individual ROI. We compared the performance of two methods of template construction. In method 1, the ROI template value was obtained from a conventionally reconstructed (without PVEC) image. In method 2, the ROI template value was directly (bypassing the PVE-affected conventional image) calculated from projections using region-based reconstruction. Our processing shows that method 1 resulted in consistent (activities for all 21 ROIs were improved) but relatively weak PVE compensation (errors of recovered total activities were equal to or lower than 10% for 5 ROIs only). Application of method 2 resulted in a selective (activities for 19 ROIs were improved) but considerably better compensation when compared to method 1 (errors of recovered total activities were equal to or lower than 10% for 10 ROIs).
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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 | 0.010 |
| 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 | 0.000 |
| 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 | 0.001 |
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".