Half-Time SPECT Myocardial Perfusion Imaging with Attenuation Correction
Bibliographic record
Abstract
UNLABELLED: Reducing acquisition time may improve patient throughput, increase camera efficiency, and reduce costs; reducing acquisition time also increases image noise. Newly available software controls the effects of noise by maximum a posteriori reconstruction while maintaining resolution with resolution-recovery methods. This study compares half-time (HT) gated myocardial SPECT images processed with ordered-subset expectation maximization with resolution recovery (OSEM-RR) (with and without CT-based attenuation correction [AC]) with full-time (FT) images obtained with a standard clinical protocol and reconstructed with filtered backprojection (FBP) and OSEM (with and without AC). METHODS: A total of 212 patients (mean age, 57 y; age range, 27-86 y) underwent 1-d rest/stress (99m)Tc-tetrofosmin gated SPECT. FT (12.5 min, both rest and stress) and HT (rest, 7.5 min; stress, 6.0 min) images were acquired with low-dose CT for AC in 112 patients. HT acquisitions were processed with OSEM-RR (with and without AC) using software, and FT acquisitions were processed with FBP and OSEM (with and without AC). In another 100 patients, test-retest repeatability was assessed using 2 sets of FT images (FBP reconstruction) that were acquired one immediately after the other. Radiologists unaware of the acquisition and reconstruction protocols visually assessed all reconstructed images for summed stress, summed rest, and summed difference scores and regional wall motion using a 17-segment model. Automated analysis on gated SPECT was used to determine left ventricular volumes, ejection fraction, and dilation (end-diastolic volume, end-systolic volume, left ventricular ejection fraction, and transient ischemic dilation [TID]). A clinical diagnosis was also determined. RESULTS: All measurements resulted in significant correlations (P < 0.01) between the HT and FT images. The only significant difference in mean values was for OSEM-RR plus AC; this method led to an increase in TID by 4% over FT imaging. The concordance in the clinical diagnosis for HT versus FT was 106 to 112 (kappa = 0.88) for no AC and 102 to 106 (kappa = 0.91) for AC, similar to the repeatability of FT versus FT (98/100, kappa = 0.95). CONCLUSION: HT images processed with the new algorithm provided a clinical diagnosis in concordance with that from FT images in 95% (no AC) to 96% (AC) of cases. This concordance is similar to the test-retest repeatability of FT imaging.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".