Optimization of steady‐state free precession MRI for lung ventilation imaging with <sup>19</sup>F C<sub>3</sub>F<sub>8</sub> at 1.5T and 3T
Bibliographic record
Abstract
Purpose: To optimize 19F imaging pulse sequences for perfluoropropane (C3F8) gas human lung ventilation MRI considering intrinsic in vivo relaxation parameters at both 1.5T and 3T. Methods: Optimization of the imaging parameters for both 3D spoiled gradient (SPGR) and steady‐state free precession (SSFP) 19F imaging sequences with inhaled 79% C3F8% and 21% oxygen was performed. Phantom measurements were used to validate simulations of SNR. In vivo parameter mapping and sequence optimization and comparison was performed by imaging the lungs of a healthy adult volunteer. T1 and T2* mapping was performed in vivo to optimize sequence parameters for in vivo lung MRI. The performance of SSFP and SPGR was then evaluated in vivo at 1.5T and 3T. Results: The in vivo T2* of C3F8 was shown to be dependent upon lung inflation level (2.04 ms ± 36% for residual volume and 3.14 ms ± 28% for total lung capacity measured at 3T), with lower T2* observed near the susceptibility interfaces of the diaphragm and around pulmonary blood vessels. Simulation and phantom measurements indicate that a factor of ~2‐3 higher SNR can be achieved with SSFP when compared with optimized SPGR. In vivo lung imaging showed a 1.7 factor of improvement in SNR achieved at 1.5T, while the theoretical improvement at 3T was not attained due to experimental SAR constraints, shorter in vivo T1, and B0 inhomogeneity. Conclusion: SSFP imaging provides increased SNR in lung ventilation imaging of C3F8 demonstrated at 1.5T with optimized SSFP similar to the SNR that can be obtained at 3T with optimized SPGR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".