Impact of Contamination from Scattered Photons in Singles-Mode Transmission Data on Quantitative Small-Animal PET Imaging
Bibliographic record
Abstract
UNLABELLED: In previous work, we described and validated a method of scatter correction for singles-mode transmission data using experimental preinjection data acquired with a dedicated rodent PET scanner. In the current work, we investigated the impact that our method has on the quantitative accuracy of small-animal PET. METHODS: This investigation had 3 stages. We first confirmed the general validity of our method by applying it to preinjection transmission data from a different imaging system (a larger dedicated primate scanner). For these data, we evaluated the accuracy of the reconstructed distributions of linear attenuation coefficients (mu-values). In the second stage, we applied our attenuation-map reconstruction and scatter correction procedure for postinjection transmission data acquired with the dedicated rodent scanner. For these studies, we investigated the quantitative accuracy of reconstructed emission images that use attenuation correction derived from postinjection transmission data. In the third stage, we compared our scatter correction method with 2 more commonly used alternatives (automated rescaling and segmentation of the attenuation-map images). RESULTS: For the primate scanner data, the average reconstructed mu-values with scatter correction were within 3% of the expected values for water and soft tissue, whereas uncorrected values were 19%-26% lower than their expected values. For the postinjection transmission studies, we found that the correct average mu-values and reconstructed activity concentrations consistent with well-counter measurements were obtained only when scatter correction and emission contamination correction were applied to the transmission data. We also found that our transmission scatter correction provides more accurate mu-values and better image quantification than either rescaling or segmentation. CONCLUSION: Using different imaging systems (primate and rodent) and different scanning protocols (before and after injection), we found that our transmission scatter correction is more accurate (for both reconstructed mu-values and activity concentrations) than the existing alternatives.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".