Simulation study of respiratory-induced errors in cardiac positron emission tomography/computed tomography
Bibliographic record
Abstract
Heart disease is a leading killer in Canada and positron emission tomography (PET) provides clinicians with in vivo metabolic information for diagnosing heart disease. Transmission data are usually acquired with 68Ge, although the advent of PET/CT scanners has made computed tomography (CT) an alternative option. The fast data acquisition of CT compared to PET may cause potential misregistration problems, leading to inaccurate attenuation correction (AC). Using Monte Carlo simulations and an anthropomorphic dynamic computer phantom, this study determines the magnitude and location of respiratory-induced errors in radioactivity uptake measured in cardiac PET/CT. A homogeneous tracer distribution in the heart was considered. The AC was based on (1) a time-averaged attenuation map, (2) CT maps from a single phase of the respiratory cycle, and (3) CT maps phase matched to the emission data. Circumferential profiles of the heart uptake were compared and differences of up to 24% were found between the single-phase CT-AC method and the true phantom values. Simulation results were supported by a PET/CT canine study which showed differences of up to 10% in the heart uptake in the lung-heart boundary region when comparing 68Ge- to CT-based AC with the CT map acquired at end inhalation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".