Assessment of the severity of partial volume effects and the performance of two template-based correction methods in a SPECT/CT phantom experiment
Bibliographic record
Abstract
We investigated the severity of partial volume effects (PVE), which may occur in SPECT/CT studies, and the performance of two template-based correction techniques. A hybrid SPECT/CT system was used to scan a thorax phantom that included lungs, a heart insert and six cylindrical containers of different sizes and activity concentrations. This phantom configuration allowed us to have non-uniform background activity and a combination of spill-in and spill-out effects for several compartments. The reconstruction with corrections for attenuation, scatter and resolution loss but not PVE correction accurately recovered absolute activities in large organs. However, the activities inside segmented 17-120 mL containers were underestimated by 20%-40%. After applying our PVE correction to the data pertaining to six small containers, the accuracy of the recovered total activity improved with errors ranging between 3% and 22% (non-iterative method) and between 5% and 15% (method with an iteratively updated background activity). While the non-iterative template-based algorithm demonstrated slightly better accuracy for cases with less severe PVE than the iterative algorithm, it underperformed in situations with considerable spill out and/or mixture of spill-in and spill-out effects.
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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 | 0.004 | 0.022 |
| 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 | 0.000 |
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".