Radionuclide angiography with a dedicated cardiac SPECT camera
Bibliographic record
Abstract
1164 Objectives Dedicated cardiac SPECT cameras have been developed that offer greatly improved sensitivity over traditional systems. Interest in these systems has been focused on myocardial perfusion imaging, however, measurement of ventricular function with gated blood-pool imaging remains an important test. Our objective is to compare ejection fraction (EF) measured on a dedicated cardiac camera to that measured on a traditional system. Methods Forty-three patients undergoing a gated blood-pool exam at our center were recruited to the study. All patients were imaged according to our standard clinical protocol. Red blood cells were labeled with 1100 MBq of pertechnetate using a modified in vivo approach. 24-frame gated planar images were obtained in an left-anterior-oblique (LAO) orientation for 10 min on a standard gamma camera (Cardial, GE Healthcare). Immediately following the standard acquisition, patients were positioned in the Discovery NM 530c (GE Healthcare) dedicated cardiac SPECT camera. 24-frame ECG-gated data were obtained for 8 min. The gated image volumes were iteratively reconstructed on a Xeleris 2.0 workstation and then transferred offline. In-house software was used to reproject the images into a 24-frame gated planar format at an angle approximately the same as that used for the clinical planar study. Both gated planar data sets were then evaluated using the FUGA semi-automated gated blood-pool analysis program on a Hermes workstation to determine the ejection fraction. Results Two studies were discarded due to poor quality of either the traditional images (n=1) or the dedicated cardiac images (n=2). The difference in ejection fraction averaged 0.3% with a standard deviation of 5%. The correlation between the two EF measurements was excellent (r2 =0.87). The linear regression of the data had a slope of 1.0 and an intercept of 0.4%. Conclusions Reprojection of 24-frame gated blood-pool SPECT images is an effective means of obtaining an EF from a dedicated cardiac SPECT camera using standard 2D-planar analysis tools
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".