Sci‐Sat AM(1): Imaging‐04: Respiratory errors in cardiac PET/CT with manual alignment of the CT image
Bibliographic record
Abstract
Respiratory motion can produce misregistration errors between CT and PET images in cardiac PET/CT imaging. The objective of this study was to determine if manual registration of a single-phase end-expiration CT scan to the PET image would eliminate respiratory-induced artifacts. Listmode data from 71 cardiac PET patient scans were rebinned into a 8-frame respiratory-gated image series based on a respiratory trigger signal obtained with an optical tracking system. CT-based attenuation correction (AC) was performed after registering the CT image with the mean position of the PET images. The 8 phases of the gated PET study were coregistered and the breathing motion was measured. Images from end-inspiration and end-expiration were compared to assess the effect of motion. Studies in which the motion was >8mm were reconstructed again, with the CT scan aligned to end-expiration or end-inspiration, to determine if phase-specific registration could reduce the residual errors. The motion was found to be greatest in the axial direction (mean 4.1mm +\- 1.8mm) and 4 Rb stress studies (17%) had motion >8mm. The maximum displacement during breathing was greater for Rb-stress imaging (<15mm) than for resting (<7.5mm) or NH3-stress (<5.4mm) imaging. No significant differences were noted between the respiratory phases of the rest studies. Errors in myocardial radiotracer uptake of up to 35% were noted between end-inspiration and end-expiration for studies with >8mm of motion. Phase-specific registration of the CT reduced the extent of the errors but did not fully resolve them, suggesting that more sophisticated AC is required.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.038 |
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".