Quantitative assessment of pulmonary artery occlusion using lung dynamic perfusion CT
Bibliographic record
Abstract
Quantitative measurement of lung perfusion is a promising tool to evaluate lung pathophysiology as well as to assess disease severity and monitor treatment. However, this novel technique has not been adopted clinically due to various technical and physiological challenges; and it is still in the early developmental phase where the correlation between lung pathophysiology and perfusion maps is being explored. The purpose of this research work is to quantify the impact of pulmonary artery occlusion on lung perfusion indices using lung dynamic perfusion CT (DPCT). We performed Lung DPCT in ten anesthetized, mechanically ventilated juvenile pigs (18.6-20.2 kg) with a range of reversible pulmonary artery occlusions (0%, 40-59%, 60-79%, 80-99%, and 100%) created with a balloon catheter. For each arterial occlusion, DPCT data was analyzed using first-pass kinetics to derive blood flow (BF), blood volume (BV) and mean transit time (MTT) perfusion maps. Two radiologists qualitatively assessed perfusion maps for the presence or absence of perfusion defects. Perfusion maps were also analyzed quantitatively using a linear segmented mixed model to determine the thresholds of arterial occlusion associated with perfusion derangement. Inter-observer agreement was assessed using Kappa statistics. Correlation between arterial occlusion and perfusion indices was evaluated using the Spearman-rank correlation coefficient. Our results determined that perfusion defects were detected qualitatively in BF, BV and MTT perfusion maps for occlusions larger than 55%, 80% and 55% respectively. Inter-observer agreement was very good with Kappa scores > 0.92. Quantitative analysis of the perfusion maps determined the arterial occlusion threshold for perfusion defects was 50%, 76% and 44% for BF, BV and MTT respectively. Spearman-rank correlation coefficients between arterial occlusion and normalized perfusion values were strong (- 0.92, - 0.72, and 0.78 for BF, BV and MTT, respectively) and were statically significant (p < 0.01). These findings demonstrate that lung DPCT enables quantification and stratification of pulmonary artery occlusion into three categories: mild, moderate and severe. Severe (occlusion ≥ 80%) alters all perfusion indices; mild (occlusion < 55%) has no detectable effect. Moderate (occlusion 55-80%) impacts BF and MTT but BV is preserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".