Respiration-Averaged CT for Attenuation Correction in Canine Cardiac PET/CT
Bibliographic record
Abstract
UNLABELLED: Heart disease is a leading cause of death in North America. With the increased availability of PET/CT scanners, CT is now commonly used as a transmission source for attenuation correction. Because of the differences in scan duration between PET and CT, respiration-induced motion can create inconsistencies between the PET and CT data and lead to incorrect attenuation correction and, thus, artifacts in the final reconstructed PET images. This study compared respiration-averaged CT and 4-dimensional (4D) CT for attenuation correction of cardiac PET in an in vivo canine model as a means of removing these inconsistencies. METHODS: Five dogs underwent respiration-gated cardiac (18)F-FDG PET and 4D CT. The PET data were reconstructed with 3 methods of attenuation correction that differed only in the CT data used: The first method was single-phase CT at either end-expiration, end-inspiration, or the middle of a breathing cycle; the second was respiration-averaged CT, which is CT temporally averaged over the entire respiratory cycle; and the third was phase-matched CT, in which each PET phase is corrected with the matched phase from 4D CT. After reconstruction, the gated PET images were summed to produce an ungated image. Polar plots of the PET heart images were generated, and percentage differences were calculated with respect to the phase-matched correction for each dog. The difference maps were then averaged over the 5 dogs. RESULTS: For single-phase CT correction at end-expiration, end-inspiration, and mid cycle, the maximum percentage differences were 11% +/- 4%, 7% +/- 3%, and 5% +/- 2%, respectively. Conversely, the maximum difference for attenuation correction with respiration-averaged CT data was only 1.6% +/- 0.7%. CONCLUSION: Respiration-averaged CT correction produced a maximum percentage difference 7 times smaller than that obtained with end-expiration single-phase correction. This finding indicates that using respiration-averaged CT may accurately correct for attenuation on respiration-ungated cardiac PET.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".