3D Model-based partial volume correction for cardiac PET imaging in mice
Bibliographic record
Abstract
204 Objectives Quantitative cardiac PET imaging is limited by spatial resolution. In this study a 3D partial volume correction (PVC) algorithm is developed for mouse LV myocardial imaging. Methods A 3D model of the ECG-gated cardiac geometry and activity was estimated and convolved with the scanner PSF. Regional parameters of the model (LV myocardium, blood and background activity, and epi/endocardial wall borders) were varied to fit measured image data. ECG gated cardiac mouse PET images, with and without noise, were simulated using the MOBY phantom convolved with a Gaussian PSF. Images were simulated with axial pixel sizes of 0.2 and 0.8 mm and resolutions of 1.25 and 1.8 mm. Images were generated with the LV oriented axially and at 20 degrees off-axis. The algorithm was evaluated in six healthy mice injected with 18F-FDG, scanned with the Inveon and reconstructed with 8 ECG gates. Results The PVC reduced bias in LV myocardial activity from 35% to within 5% and decreased the COV from 11-12% to 6-9% on the simulated images, demonstrating the ability to simultaneously improve recovery and homogeneity. With the heart oriented on-axis, an axial resolution of 1.8 mm did not affect the corrected activity, however an axial pixel size of 0.8 mm caused a 3% underestimation (p Conclusions PVC is necessary to restore quantitative accuracy in cardiac imaging. The 3D PVC algorithm explored in this study appears to be a feasible solution to improve quantitative accuracy in mouse heart imaging with PET. Research Support NSERC, CIH
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".