Hybrid SPECT/cardiac‐gated first‐pass perfusion CT: locating transplanted cells relative to infarcted myocardial targets
Bibliographic record
Abstract
PURPOSE: A challenge with cardiac cell therapy is determining the location of cells relative to infarct tissue. As cells are viable following ¹¹¹In-labeling, and first-pass CT imaging can identify regions of myocardial infarction, we evaluated the feasibility of a SPECT/CT system to localize cells relative to infarcted myocardium in a canine model. METHODS: Ten canines underwent surgical ligation of the left-anterior-descending artery and endothelial progenitor cells labeled with ¹¹¹In-tropolone were transplanted endocardially or epicardially. SPECT/CT was performed on day of transplantation, 4 and 10 days post-transplantation. For each imaging session first-pass perfusion CT was performed to delineate the area of reduced perfusion. SPECT and first-pass CT images were fused and evaluated. Contrast-to-noise ratios (CNR) were calculated for ¹¹¹In-SPECT images to evaluate cell detection. RESULTS: The zone of reduced perfusion was well delineated on first-pass perfusion CT in all canines. The ¹¹¹In signal was visualized within this zone in all cases. Analysis of the CNRs suggests that cells may be followed for 11 effective half-lives using the images from first-pass perfusion CT to provide the anatomic landmarks. CONCLUSION: In the setting of an acute myocardial infarction SPECT/[first-pass perfusion CT] is an effective hybrid platform for the localization of cells in relation to the area of reduced blood flow.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".