A framework for Fourier‐decomposition free‐breathing pulmonary <sup>1</sup>H MRI ventilation measurements
Bibliographic record
Abstract
Purpose To develop a rapid Fourier decomposition (FD) free‐breathing pulmonary 1 H MRI (FDMRI) image processing and biomarker pipeline for research use. Methods We acquired MRI in 20 asthmatic subjects using a balanced steady‐state free precession (bSSFP) sequence optimized for ventilation imaging. 2D 1 H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right‐to‐left lung volume–ratio. The segmented lung series were co‐registered using a coarse‐to‐fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation‐weighted maps and ventilation‐defect‐percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung‐masks, deformed manual lung‐masks, and fiducials in the moving and fixed images using Dice‐similarity‐coefficient (DSC), mean‐absolute‐distance (MAD), and target‐registration‐error (TRE). The relationship of FD‐VDP and 3 He‐VDP was evaluated using the Pearson‐correlation‐coefficient ( r ) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient‐of‐variation (CoV) and intra‐class‐correlation‐coefficient (ICC) for segmentation, registration, and FD‐VDP components. Results For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD‐VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD‐VDP was correlated with 3 He‐VDP ( r = 0.69, P < 0.001) although there was a bias toward lower FD‐VDP (bias = −4.9%). Conclusions We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.
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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 | 0.000 |
| 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) | 0.001 | 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".