Quantification of lung water density with UTE Yarnball MRI
Bibliographic record
Abstract
Purpose An efficient Yarnball ultrashort‐TE k‐space trajectory, in combination with an optimized pulse sequence design and automated image‐processing approach, is proposed for fast and quantitative imaging of water density in the lung parenchyma. Methods Three‐dimensional Yarnball k‐space trajectories (TE = 0.07 ms) were designed at 3 T for breath‐hold and free‐breathing navigator acquisitions targeting the lung parenchyma (full torso spatial coverage) with minimal T 1 and weighting. A composite of all solid tissues surrounding the lungs (muscle, liver, heart, blood pool) was used for user‐independent lung water density signal referencing and B 1 ‐inhomogeneity correction needed for the calculation of relative lung water density images. Sponge phantom experiments were used to validate absolute water density quantification, and relative lung water density was evaluated in 10 healthy volunteers. Results Phantom experiments showed excellent agreement between sponge wet weight and imaging‐derived water density. Breath‐hold (13 seconds) and free‐breathing (~2 minutes) Yarnball acquisitions in volunteers (2.5‐mm isotropic resolution) had negligible artifacts and good lung parenchyma SNR (>10). Whole‐lung average relative lung water density values with fully automated analysis were 28.2 ± 1.9% and 28.6 ± 1.8% for breath‐hold and free‐breathing acquisitions, respectively, with good test–retest reproducibility (intraclass correlation coefficient = 0.86 and 0.95, respectively). Conclusions Quantitative lung water density imaging with an optimized Yarnball k‐space acquisition approach is possible in a breath‐hold or short free‐breathing study with automated signal referencing and segmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".