Toward a practical template-based approach to semiquantitative SPECT myocardial perfusion imaging
Bibliographic record
Abstract
PURPOSE: Our template-based quantitative perfusion single photon emission computed tomography (SPECT) method (T-QPS) performs semiquantitative analysis for myocardial perfusion imaging (MPI) without the use of normal databases. However, in its current form, T-QPS requires extensive calculations, which limits its clinical application. In the interest of clinical feasibility, the authors examine the trade-off between accuracy and processing time as the method is simplified. METHODS: The T-QPS method uses the reconstructed SPECT image of the patient to create a 3D digital template of his∕her healthy heart. This template is then projected, reconstructed, and sampled into the bulls-eye map domain. A ratio of the patient and template images produces a final corrected image in which a threshold is applied to identify perfusion defects. In principle, the template should be constructed with the heart and all extracardiac activity, and the projection step should include primary and scatter components; however, this leads to lengthy calculations. In an attempt to shorten the processing time, the authors analyzed the performance of four template (T) generation methods: T(P-HRT), T(PS-HRT), T(P-HRTBKG), and T(PS-HRTBKG), where P and S represent primary and scattered photons included in the projection step, respectively; and HRT and HRTBKG represent template constructed with the heart only and the heart with background activity, respectively. Forty-eight thorax phantoms and 21 randomly selected patient studies were analyzed using each approach. All studies used GE's Infinia Hawkeye SPECT∕CT system and followed a standard cardiac acquisition protocol. RESULTS: Approximate processing times for the T(P-HRT), T(PS-HRT), T(P-HRTBKG), and T(PS-HRTBKG) methods were less than a minute, 2-3 h, less than a minute and 3-4 h, respectively. In both the simulation and patient studies, a significant reduction in the quality of perfusion defect definition was exhibited by the T(P-HRT) method relative to the other three methods. The optimal method with respect to perfusion defect definition and processing time was T(P-HRTBKG) with a sensitivity, specificity, and accuracy in spatially defining the perfusion defects (simulation study) of 80%, 84%, and 83%, respectively. CONCLUSIONS: The T-QPS method using T(P-HRTBKG) leads to accurate and fast semiquantitative analysis of SPECT MPI, without the use of normal databases.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".