Lung morphometry using hyperpolarized <sup>129</sup>Xe apparent diffusion coefficient anisotropy in chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE: The goal of this work was to investigate lung morphological changes associated with chronic obstructive pulmonary disease (COPD) using hyperpolarized (129) Xe diffusion-weighted MRI. METHODS: Hyperpolarized (129) Xe MRI was performed at three different nonzero diffusion sensitizations (b-value = 12, 20, and 30 s/cm(2) ) in the lungs of four subjects with COPD and four healthy volunteers. The image signal intensities were fit as a function of b-value to obtain anisotropic diffusion coefficient maps for all subjects. The image signal intensities were also fit to a morphological model allowing extraction of length scales associated with the terminal airways: external radius (R), internal radius (r), mean airspace chord length (Lm ), and depth of alveolar sleeve (h). RESULTS: Longitudinal (DL ) and transverse (DT ) anisotropic diffusion coefficients were both significantly increased (both P= 0.004) in the COPD subjects (0.102 ± 0.02 cm(2) /s and 0.072 ± 0.02 cm(2) /s, respectively) compared with the healthy subjects (0.083 ± 0.011 cm(2) /s and 0.046 ± 0.017 cm(2) /s, respectively). Significant morphological differences were observed between the COPD subjects and healthy volunteers, specifically decreases in h (68 ± 36 µm vs. 95 ± 710 µm, respectively, P = 0.019) and increases in Lm (352 ± 57 µm vs. 253 ± 37 µm, respectively, P = 0.002) consistent with values obtained previously using hyperpolarized (3) He MRI in similar subjects. CONCLUSIONS: Diffusion-weighted hyperpolarized (129) Xe MRI is a promising technique for mapping changes in human lung morphology and may be useful for early detection of emphysema associated with COPD.
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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 | 0.000 | 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 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".