Mapping and quantifying hyperpolarized <sup>3</sup>He magnetic resonance imaging apparent diffusion coefficient gradients
Bibliographic record
Abstract
We measured hyperpolarized 3He magnetic resonance imaging (MRI) apparent diffusion coefficients (ADC) and quantified ADC gradients in each three-by-three voxel region of interest (ROI). Such local ADC gradients can be represented in vector maps showing the magnitude (|G3x3|) and direction of ADC gradients, providing a qualitative visualization tool and quantitative measurement of airway and air space heterogeneity. Twenty-four subjects (15 male, mean age=67+/-7 yr) with global initiative for chronic obstructive lung disease (GOLD) stage II (n=9, mean age 68+/-6 yr), GOLD stage III chronic obstructive pulmonary disease (COPD; n=7, mean age 67+/-8 yr), and age-matched healthy volunteers (n=8, mean age 67+/-6 yr) were enrolled based on their age and spirometry results. Hyperpolarized 3He MRI was performed on a whole body 3.0 Tesla system. Mean 3He ADC and ADC standard deviation were calculated for the center coronal slice, and the mean magnitude and direction of the ADC gradient vectors were calculated for each three-by-three voxel matrix (|G3x3|). While the 3He ADC standard deviation was not significantly different, mean |G3x3| was significantly different between subjects with stage II (0.14+/-0.03 cm/s) and stage III COPD (0.19+/-0.03 cm/s; P<0.005) and between healthy subjects (0.12+/-0.03 cm/s) and those with stage II COPD (P<0.02). The second order statistic |G3x3| may provide a sensitive measure of ADC heterogeneity for ROI representing 9.4x9.4x30 mm or 2.6 cm3 of lung tissue.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".