Registration pipeline for pulmonary free-breathing <sup>1</sup>H MRI ventilation measurements
Bibliographic record
Abstract
Objectives: Our aim was to develop a clinically-practical and physiologically-relevant approach for regional structure-function measurements of the lung using Fourier decomposition of free-breathing pulmonary magnetic resonance imaging (FDMRI). Methods: Ten patients with chronic obstructive pulmonary disease provided written informed consent to a study protocol approved by Health Canada and completed pulmonary function tests, 1H/hyperpolarized noble gas and free-breathing pulmonary magnetic resonance imaging (MRI) during a single 2-hour visit. Free-breathing <sup>1</sup>H MRI was simultaneously segmented using a multi-region coupled continuous max-flow approach by exploring primal/dual analysis and convex optimization techniques. The segmented free-breathing 1H MRI lung was registered using deformable registration approach that was developed using dual and convex optimization methods to compensate for respiratory/cardiac motion. Fourier decomposition of the co-registered lung was used to generate pulmonary functional information that was quantified as ventilation-defect-percent (VDP). The pipeline was implemented on a GPU for speed-up. Lung segmentation accuracy was measured by comparing algorithm and manual lung masks using Dice-similarity-coefficient (DSC). FD-VDP was compared to <sup>3</sup>He-VDP using Pearson correlation coefficient and Bland-Altman analysis. The reproducibility of our algorithm was measured using coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for DSC and FD-VDP. Results: The pipeline yielded a whole lung DSC of 95.7±1.7% and FD-VDP that were correlated with <sup>3</sup>He-VDP (r = 0.81, p = 0.004). CoV (ICC) were 0.4% (0.98) and 4.1% (0.98) for whole lung DSC and FD-VDP, respectively. The proposed approach requires ~45 min for parallel implementation with minimal user interaction. Conclusion: The proposed approach provides a clinically-practical pipeline to generate regional pulmonary structure-function measurements using free-breathing pulmonary <sup>1</sup>HMRI with promising potential for widespread clinical translation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".