Automated pulmonary lobar ventilation measurements using volume-matched thoracic CT and MRI
Bibliographic record
Abstract
Objectives: To develop and evaluate an automated registration and segmentation pipeline for regional lobar pulmonary structure-function measurements, using volume-matched thoracic CT and MRI in order to guide therapy. Methods: Ten subjects underwent pulmonary function tests and volume-matched 1H and 3He MRI and thoracic CT during a single 2-hr visit. CT was registered to 1H MRI using an affine method that incorporated block-matching and this was followed by a deformable step using free-form deformation. The resultant deformation field was used to deform the associated CT lobe mask that was generated using commercial software. 3He-1H image registration used the same two-step registration method and 3He ventilation was segmented using hierarchical k-means clustering. Whole lung and lobar 3He ventilation and ventilation defect percent (VDP) were generated by mapping ventilation defects to CT-defined whole lung and lobe volumes. Target CT-3He registration accuracy was evaluated using region- , surface distance- and volume-based metrics. Automated whole lung and lobar VDP was compared with semi-automated and manual results using paired t-tests. Results: The proposed pipeline yielded regional spatial agreement of 88.0±0.9% and surface distance error of 3.9±0.5 mm. Automated and manual whole lung and lobar ventilation and VDP were not significantly different and they were significantly correlated (r = 0.77, p < 0.0001). Conclusion: The proposed automated pipeline can be used to generate regional pulmonary structural-functional maps with high accuracy and robustness, providing an important tool for image-guided pulmonary interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".