Patient motion on the GE Discovery CZT camera: Investigating the necessity of motion correction
Bibliographic record
Abstract
2114 Objectives Patient motion of >=13mm (2 pixels) has been shown to cause artifacts that may be misinterpreted as ischemia or scarring due to coronary artery disease. New dedicated cardiac cameras allow faster acquisition times, potentially decreasing patient motion. However, the new cameras simultaneously acquire all projection views and thus cannot provide the rotating cine view typically used with traditional SPECT cameras to detect and correct for motion. Knowledge of patient motion on new cameras is needed to guide development of acquisition protocols and quality assurance procedures. This investigation was undertaken to determine when patient motion initially occurs during acquisition on a dedicated cardiac SPECT camera and if the time of motion correlates with patient age. Methods 100 cardiac stress acquisitions acquired in list mode for 8min on the GE Discovery NM 530c CZT camera were retrospectively studied. Patients were injected with 900MBq of 99mTc-tetrofosmin as part of standard clinical SPECT imaging and consented to further images on the CZT camera. Patients were consecutively selected to provide an approximately equal number of cases for age categories 70. Patient motion was quantified by displacement of the epicardium reconstructed in transverse and sagittal slices at each 1min interval over the 8min study. Significant motion was considered to be anything >=12mm (3 pixels). Results Aggregate analysis of the 100 studies showed 10% had significant motion after 4 minutes of acquisition. Motion significantly increased (p Conclusions These results suggest that image acquisition times of
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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) | 0.000 | 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 | 0.000 |
| 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".