Effects of CT‐based attenuation correction of rat microSPECT images on relative myocardial perfusion and quantitative tracer uptake
Bibliographic record
Abstract
PURPOSE: Our goal in this work was to investigate the impact of CT-based attenuation correction on measurements of rat myocardial perfusion with (99m)Tc and (201)Tl single photon emission computed tomography (SPECT). METHODS: Eight male Sprague-Dawley rats were injected with (99m)Tc-tetrofosmin and scanned in a small animal pinhole SPECT/CT scanner. Scans were repeated weekly over a period of 5 weeks. Eight additional rats were injected with (201)Tl and also scanned following a similar protocol. The images were reconstructed with and without attenuation correction, and the relative perfusion was analyzed with the commercial cardiac analysis software. The absolute uptake of (99m)Tc in the heart was also quantified with and without attenuation correction. RESULTS: For (99m)Tc imaging, relative segmental perfusion changed by up to +2.1%/-1.8% as a result of attenuation correction. Relative changes of +3.6%/-1.0% were observed for the (201)Tl images. Interscan and inter-rat reproducibilities of relative segmental perfusion were 2.7% and 3.9%, respectively, for the uncorrected (99m)Tc scans, and 3.6% and 4.3%, respectively, for the (201)Tl scans, and were not significantly affected by attenuation correction for either tracer. Attenuation correction also significantly increased the measured absolute uptake of tetrofosmin and significantly altered the relationship between the rat weight and tracer uptake. CONCLUSIONS: Our results show that attenuation correction has a small but statistically significant impact on the relative perfusion measurements in some segments of the heart and does not adversely affect reproducibility. Attenuation correction had a small but statistically significant impact on measured absolute tracer uptake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".