An 8‐element Tx/Rx array utilizing MEMS detuning combined with 6 Rx loops for <sup>19</sup>F and <sup>1</sup>H lung imaging at 1.5T
Bibliographic record
Abstract
Purpose To firstly improve the attainable image SNR of 19F and 1H C3F8 lung imaging at 1.5 tesla using an 8‐element transmit/receive (Tx/Rx) flexible vest array combined with a 6‐element Rx‐only array, and to secondly evaluate microelectromechanical systems for switching the array elements between the 2 resonant frequencies. Methods The Tx efficiency and homogeneity of the 8‐element array were measured and simulated for 1H imaging in a cylindrical phantom and then evaluated for in vivo 19F/1H imaging. The added improvement provided by the 6‐element Rx‐only array was quantified through simulation and measurement and compared to the ultimate SNR. It was verified through the measurement of isolation that microelectromechanical systems switches provided broadband isolation of Tx/Rx circuitry such that the 19F tuned Tx/Rx array could be effectively used for both 19F and 1H nuclei. Results For 1H imaging, the measured Tx efficiency/homogeneity (mean ± percent SD; ) was comparable to that simulated ( ). The 6 additional Rx‐only loops increased the mean Rx sensitivity when compared to the 8‐element array by a factor of 1.41× and 1.45× in simulation and measurement, respectively. In regions central to the thorax, the simulated SNR of the 14‐element array achieves ≥70% of the ultimate SNR when including noise from the matching circuits and preamplifiers. A measured microelectromechanical systems switching speed of 12 µs and added minimum 22 dB of isolation between Tx and Rx were sufficient for Tx/Rx switching in this application. Conclusion The described single‐tuned array driven at 19F and 1H, utilizing microelectromechanical systems technology, provides excellent results for 19F and 1H dual‐nuclear lung ventilation imaging.
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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 | 0.000 |
| 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 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".