Accelerated interleaved spiral‐IDEAL imaging of hyperpolarized <sup>129</sup>Xe for parametric gas exchange mapping in humans
Bibliographic record
Abstract
Purpose To demonstrate the feasibility of mapping gas exchange with single breath‐hold hyperpolarized (HP) 129Xe in humans, acquiring parametric maps of lung physiology. The potential benefit of acceleration using parallel imaging for this application is also explored. Methods Six healthy volunteers were scanned with a modified spiral‐IDEAL sequence to acquire gas exchange‐weighted images using a single dose of 129Xe. These images were fit with the model of xenon exchange (MOXE) on a voxel‐wise basis calculating parametric maps of lung physiology, specifically: air–capillary barrier thickness (δ), alveolar septal thickness (d), capillary transit time (tx), pulmonary hematocrit (HCT), and alveolar surface area‐to‐volume ratio (SVR). An accelerated version of the sequence was also tested in subset of 4 volunteers and compared to the fully sampled (FS) results. Results Mean image‐wide values calculated from MOXE parametric maps derived from FS dissolved 129Xe spiral‐IDEAL images were: δ = 0.89 ± 0.17 μm, d = 7.5 ± 0.5 μm, tx = 1.1 ± 0.2s, HCT = 28.8 ± 2.3%, and SVR = 140 ± 16 cm−1, in good agreement with previously published values based on whole‐lung spectroscopy of healthy human subjects. Parallel imaging sufficiently reduces artifacting in accelerated images, but increases disagreement with MOXE parameters derived from FS data with mean voxel‐wise unsigned relative differences of: δ = 39 ± 9%, d = 22 ± 3%, tx = 117 ± 43%, HCT = 11 ± 2%, and SVR = 31 ± 12%. Conclusion Dissolved HP 129Xe spiral‐IDEAL imaging for gas exchange mapping is feasible in humans using a single breath‐hold. Accelerated gas exchange mapping is also shown to be feasible but requires further improvements to increase quantitative accuracy.
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 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 | 0.001 |
| 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 | 0.000 | 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 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".