A template‐based approach to semi‐quantitative SPECT myocardial perfusion imaging: Independent of normal databases
Bibliographic record
Abstract
PURPOSE: Normal patient databases (NPDs) are used to distinguish between normal and abnormal perfusion in SPECT myocardial perfusion imaging (MPI) and have gained wide acceptance in the clinical environment, yet there are limitations to this approach. This study introduces a template-based method for semi-quantitative MPI, which attempts to overcome some of the NPD limitations. METHODS: Our approach involves the construction of a 3D digital healthy heart template from the delineation of the patient's left ventricle in the SPECT image. This patient-specific template of the heart, filled with uniform activity, is then analytically projected and reconstructed using the same algorithm as the original image. Subsequent to generating bulls-eye maps for the patient image (PB) and the template image (TB), a ratio (PB/TB) is calculated, which produces a reconstruction-artifact corrected image (CB). Finally, a threshold is used to define defects within CB enabling measurements of the perfusion defect extent (EXT). The SPECT-based template (Ts) measurements were compared to those of a CT-based "ideal" template (TI). Twenty digital phantoms were simulated: male and female, each with one healthy heart and nine hearts with various defects. Four physical phantom studies were performed modeling a healthy heart and three hearts with different defects. The phantom represented a thorax with spine, lung, and left ventricle inserts. Images were acquired on General Electric's (GE) Infinia Hawkeye SPECT/CT camera using standard clinical MPI protocol. Finally, our method was applied to 14 patient MPI rest/stress studies acquired on the GE Infinia Hawkeye SPECT/CT camera and compared to the results obtained from Cedars-Sinai's QPS software. RESULT: In the simulation studies, the true EXT correlated well with the TI (slope= 1.08; offset = -0.40%; r = 0.99) and Ts (slope = 0.90; offset = 0.27%; r = 0.99) methods with no significant differences between them. Similarly, strong correlations were measured for EXT obtained from QPS and the template method for patient studies (slope =0.91; offset = 0.45%; r = 0.98). Mean errors in extent for the Ts method using simulation, physical phantom, and patient data were 2.7% +/- 2.4%, 0.9% +/- 0.5%, 2.0% +/- 2.7%, respectively. CONCLUSIONS: The authors introduced a method for semi-quantitative SPECT MPI, which offers a patient-specific approach to define the perfusion defect regions within the heart, as opposed to the patient-averaged NPD methodology.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".