Improvement of myocardial perfusion defect severity quantitation in cardiac SPECT: A simulation study
Bibliographic record
Abstract
We aim at improving the quantitative assessment of the severity of myocardial perfusion defects in cardiac SPECT imaging. The idea of a numerical heart template is utilized, which enables a patient specific measurement of defect severity as opposed to the more traditional population-based approaches. Using NCAT we developed three male thorax phantoms with different orientations and sizes of the left ventricle. Each heart contained a small (5%) inferior wall defect with a severity of 20-80%. The SimSET code was used to perform 21 simulations modeling cardiac SPECT acquisitions with a Tc-99m radiotracer, LEHR collimator, 64×64 matrix, and 60 camera stops. A conventional method (CM) of defect severity assessment included the MLEM reconstruction with 40 iterations and a calculation of the ratio, RCM, of the average activity concentrations in the defect and the normal heart. Our template method (TM) calculates a new ratio, RTM, which is a correction to RCM by rescaling it between two reference levels corresponding to a completely non-perfused defect and a healthy myocardium. These levels are calculated by projecting and reconstructing two numerical heart templates (may be based on CT in clinical studies) with activity ratios in defect to normal heart set to zero and unity, respectively. The proposed TM method was more accurate and more sensitive to small changes in defect severity than CM. While CM showed no defect (RCM was equal to 0.98-1.02) in the case with 20% severity (true ratio is 0.8), TM led to RTM values of 0.84-0.92. On average, our TM technique exhibited a 17% improvement in defect to normal ratios relative the CM method. Our proposed method offers a patient-specific assessment of perfusion defect severity in SPECT without the limitations intrinsic to traditional methodologies (e.g. extreme heart geometries).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".