Calculating Relative Lung Perfusion Using Fluoroscopic Sequences and Image Analysis: The Fluoroscopic Flow Calculator
Bibliographic record
Abstract
BACKGROUND: Maldistribution of pulmonary blood flow in patients with congenital heart disease impacts exertional performance and pulmonary artery growth. Currently, measurement of relative pulmonary perfusion can only be performed outside the catheterization laboratory. We sought to develop a tool for measuring relative lung perfusion using readily available fluoroscopy sequences. METHODS: A retrospective cohort study was conducted on patients with conotruncal anomalies who underwent lung perfusion scans and subsequent cardiac catheterizations between 2011 and 2022. Inclusion criteria were nonselective angiogram of pulmonary vasculature, oblique angulation ≤20°, and an adequate view of both lung fields. A method was developed and implemented in 3D Slicer’s SlicerHeart extension to calculate the amount of contrast that entered each lung field from the start of contrast injection and until the onset of levophase. The predicted perfusion distribution was compared with the measured distribution of pulmonary blood flow and evaluated for correlation, accuracy, and bias. RESULTS: In total, 32% (79/249) of screened studies met the inclusion criteria. A strong correlation between the predicted flow split and the measured flow split was found ( R 2 =0.83; P <0.001). The median absolute error was 6%, and 72% of predictions were within 10% of the true value. Bias was not systematically worse at either extreme of the flow distribution. The prediction was found to be more accurate for either smaller and younger patients (age 0–2 years), for right ventricle injections, or when less cranial angulations were used (≤20°). In these cases (n=40), the prediction achieved R 2 =0.87, median absolute error of 5.5%, and 78% of predictions were within 10% of the true flow. CONCLUSIONS: The current study demonstrates the feasibility of a novel method for measuring relative lung perfusion using conventional angiograms. Real-time measurement of lung perfusion at the catheterization laboratory has the potential to reduce unnecessary testing, associated costs, and radiation exposure. Further optimization and validation is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".