Evaluation of fluorine‐19 magnetic resonance imaging of the lungs using octafluorocyclobutane in a rat model
Bibliographic record
Abstract
PURPOSE: To test octafluorocyclobutane (OFCB) as an inhalation contrast agent for fluorine-19 MRI of the lung, and to compare the image quality of OFCB scans with perfluoropropane (PFP) scans THEORY AND METHODS: After normalizing for the number of signal averages, a theoretical comparison between the OFCB signal-to-noise ratio (SNR) and PFP SNR predicted the average SNR advantage of 90% using OFCB during gradient echo imaging. The OFCB relaxometry was conducted using single-voxel spectroscopy and spin-echo imaging. A comparison of OFCB and PFP SNRs was performed in vitro and in vivo. Five healthy Sprague-Dawley rats were imaged during single breath-hold and continuous breathing using a Philips Achieva 3.0T MRI scanner (Philips, Andover, MA). The scan time was constant for both gases. Statistical comparison between PFP and OFCB scans was conducted using a paired t test and by calculating the Bayes factor. RESULTS: by 20%. The OFCB in vivo images showed 73% higher normalized SNR on average compared with images acquired using PFP. The statistical significance was shown by both paired t test and calculated Bayes factors. The experimental results agree with theoretical calculations within the error of the relaxation parameter measurements. CONCLUSION: The quality of the lung images acquired using OFCB was significantly better compared with PFP scans. The OFCB images had higher a SNR and were artifact-free.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".