Implementation of an iterative scatter correction, the influence of attenuation map quality and their effect on absolute quantitation in SPECT
Bibliographic record
Abstract
We investigated the accuracy of qSPECT, a quantitative SPECT reconstruction algorithm we have developed which employs corrections for collimator blurring, photon attenuation and scatter, and provides images in units of absolute radiotracer concentrations (kBq cm(-3)). Using simulated and experimental phantom data with characteristics similar to clinical cardiac perfusion data, we studied the implementation of a scatter correction (SC) as part of an iterative reconstruction protocol. Additionally, with experimental phantom studies we examined the influence of CT-based attenuation maps, relative to those obtained from conventional SPECT transmission scans, on SCs and quantitation. Our results indicate that the qSPECT estimated scatter corrections did not change appreciably after the third iteration of the reconstruction. For the simulated data, qSPECT concentrations agreed with images reconstructed using ideal, scatter-free, simulated data to within 6%. For the experimental data, we observed small systematic differences in the scatter fractions for data using different combinations of SCs and attenuation maps. The SCs were found to be significantly influenced by errors in image coregistration. The reconstructed concentrations using CT-based corrections were more quantitatively accurate than those using attenuation maps from conventional SPECT transmission scans. However, segmenting the attenuation maps from SPECT transmission scans could provide sufficient accuracy for most applications.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".