Sci-Fri AM: Imaging - 09: Hybrid SPECT and First-Pass Perfusion CT: Application in Cell Localization
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 and track cells relative to infarcted myocardium in a canine model. Methods: To determine accuracy of SPECT/CT registration, images were acquired of capillary tubes filled with CT and SPECT contrast agents. Accuracy was measured by comparing locations of tube centroids in SPECT and CT. 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 infarct zone. SPECT and first-pass CT images were fused and evaluated. Delayed-enhanced MRI was performed to validate CT infarct localization. Contrast-to-noise ratios (CNR) were calculated for 111In-SPECT images to evaluate cell detection. Results: Phantom SPECT/CT registration accuracy was approximately 1mm. The infarct zone was well delineated on first-pass perfusion CT in all canines and correlated well with MRI findings. 111In signal was visualized within the infarct zone in all cases. Analysis of the CNRs suggests that cells can be followed for 11 effective half-lives provided the location of the cells can be inferred by first-pass CT. Conclusion: SPECT/[First-Pass Perfusion CT] is an effective hybrid platform for the localization and tracking of stem cells in relation to infarct tissue.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".