Lower Extremity Flow Quantification Using Dynamic ⁸²Rb PET: A Preclinical Investigation
Bibliographic record
Abstract
Accurate assessment of regional flow in the lower extremities is crucial for managing peripheral arterial disease with critical limb ischemia. This study investigates dynamic 82Rb PET imaging with kinetic modeling for evaluating skeletal muscle flow in a porcine model of hindlimb ischemia. Five pigs with acute unilateral occlusion of the right common femoral artery were scanned at rest using two protocols: The first protocol involved two sequential injections to measure the image-derived input function (IDIF) in the left ventricle (LV) and leg blood flow. A three-parameter one-tissue compartment with spillover model estimated skeletal muscle flow in ischemic and nonischemic limbs. The effects of correcting delay and dispersion of LV-IDIF on model fitting were explored. For short axial field of view scanners, the feasibility of a single injection with shuttling between the heart and the leg was also assessed. Flow estimates ranged from 0.012 to 0.077 cm3/min/cm3 across animals and significantly decreased on ischemic muscles (p < 0.05). Delay and dispersion corrections yielded improved Akaike information criterion values and physiological consistency. However, accurate corrections were more difficult using the single injection and shuttling protocol. Future studies to optimize data acquisition are needed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".