Using hyperpolarized <sup>3</sup>He MRI to evaluate treatment efficacy in cystic fibrosis patients
Bibliographic record
Abstract
PURPOSE: To use hyperpolarized (HP) (3)He MR imaging to assess functional lung ventilation in subjects with cystic fibrosis (CF) before and after treatment. MATERIALS AND METHODS: We performed HP (3)He static ventilation MRI scans on three subjects, using a Philips 3.0 Tesla (T) Achieva MRI scanner, before and after 11 days of in-patient treatment with combined intravenous and inhaled therapies for pulmonary exacerbations of CF. We also collected spirometry data. We quantified pulmonary ventilation volume measured with HP (3)He MRI using an advanced semi-automated analysis technique. RESULTS: Following 11 days of treatment with intravenous antibiotics, hypertonic saline, and rhDNase, HP (3)He MR images in one subject displayed a 25% increase in total ventilation volume. Total ventilation volume in the other two subjects slightly decreased. All three subjects showed increases in FEV(1) and FVC following treatment. CONCLUSION: In all subjects, the HP (3)He MR images provided detailed information on precisely where in the lungs gas was reaching. These data provide additional support for the conclusion that HP noble gas MRI can be a powerful tool for evaluating lung ventilation in patients with cystic fibrosis, but also raise important questions about the correlation between spirometry and HP gas MRI measurements.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".