Single-Phase CT Aligned to Gated PET for Respiratory Motion Correction in Cardiac PET/CT
Bibliographic record
Abstract
UNLABELLED: Respiratory motion can induce artifacts in cardiac PET/CT because of the misregistration of the CT attenuation map and emission data. Some solutions to the respiratory motion problem use 4-dimensional CT, but this increases patient radiation exposure. Realignment of 3-dimensional CT and PET images can remove apparent uptake defects caused by mispositioning of the PET emission data into the lung regions on the CT scan. This realignment is typically done as part of regular clinical quality assurance. We evaluated a method to improve on this standard approach, without increasing the radiation exposure to the patient, by acquiring a respiration-gated PET scan and separately aligning the 3-dimensional CT scan to each phase of the PET study. METHODS: Three hundred ten clinical PET perfusion scans ((82)Rb [n = 187] and (13)N-ammonia [n = 123]) were retrospectively assessed. Studies were respiration-gated, and motion was measured between inspiration and expiration phases. Those studies with motion > or = 8 mm were evaluated for significant differences between inspiration and expiration. Studies with significant differences were reprocessed with the phase-alignment approach. The observed motion with (82)Rb and (13)N-ammonia for rest and stress imaging was also compared. RESULTS: Twenty-three scans (7.41%) had motion > or = 8 mm, and 9 of these had significant differences between inspiration and expiration, suggesting the presence of respiratory artifacts. Phase-aligned respiratory motion compensation reduced this difference in 8 of 9 cases (89%). No significant differences were observed between (82)Rb and (13)N-ammonia, and motion during stress imaging was correlated with motion at rest (r = 0.61, P < 0.001). CONCLUSION: Phase-aligned correction improves the consistency of PET/CT perfusion images by reducing discrepancies caused by respiratory motion. This new approach to CT-based attenuation correction has no additional patient radiation exposure and may improve the specificity of PET perfusion imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".