A Spline Model for Rv Registration from Cardiac Pet Images
Bibliographic record
Abstract
Background: The etiology of pulmonary hypertension (PAH) is poorly understood, and is associated with high morbidity and mortality (life expectancy <3 years). PAH leads to progressive enlargement of the right ventricle (RV) with a rapid decline in function. The utility of non-invasive molecular imaging to track PAH progression, evaluate disease etiology and monitor clinical therapy is currently limited by the lack of automated RV image analysis tools. Objective: To develop a highly automated RV registration, sampling, and analysis tool for human and small animal positron emission tomography (PET) imaging. Methods: We developed a spline-based model for registering the mid-RV myocardium from a PET uptake image. Model fitting was automated by optimizing a constrained cost function with optional operator intervention. Inter- and intra-operator variability of FDG PET uptake activity measurements were evaluated using a dataset consisting of 7 PAH and 12 randomly selected non-PAH human subjects. The dataset was processed twice by each of two operators, a novice and an expert. The accuracy of RV cavity volumes and ejection fraction (EF) measurements from cardiac-gated PET images was evaluated by comparing with results from cardiac magnetic resonance imaging (CMR) in 5 PAH patients. Results: 50% of cases assessed required operator intervention. In intra-operator variability analysis of relative uptake images, the reproducibility coefficient (RPC) for each operator was 5.6% and 6.4% for expert and novice respectively. Inter-operator uptake RPC was 8.2%. RV cavity volumes and EF agreed closely with CMR results (r 2 =0.954, n=10 and r 2 =0.965, n=5 respectively). Conclusions: The RV can be automatically registered in uptake PET images and has performance characteristics that are comparable with established left ventricle analysis tools making it suitable for investigating RV molecular and cardiac functions. Additional work is required to improve automation and evaluate molecular function quantification with dynamic imaging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| 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".