Litz wire radiofrequency receive coils for hyperpolarized noble gas MR imaging of rodent lungs at 73.5 mT
Bibliographic record
Abstract
Abstract Magnetic resonance imaging with hyperpolarized noble gases, 3He or 129Xe, has become a promising approach for visualizing lung anatomy and function. The polarization of hyperpolarized noble gases does not depend on the magnetic field strength of the imaging system providing an opportunity to image at magnetic field strengths considerably lower than those typically used for clinical purposes (<0.1 T). At such low fields, image noise is dominated by electronic sources, particularly those originating from the radiofrequency coils. An improvement in image signal‐to‐noise ratio (SNR) is possible at low fields by reducing radiofrequency coil noise using Litz wire. In this work, radiofrequency coils of similar geometry were constructed with either conventional copper wire or three different types of Litz wire and compared in phantoms and in vivo in rat lungs using hyperpolarized 3He and 129Xe gases. The coils were tuned at either 0.866 MHz or 2.385 MHz, corresponding to the Larmor frequencies of 129Xe and 3He at 73.5 mT. The effect of wire spacing and number of windings was investigated. A SNR improvement of up to 131% was obtained with the Litz wire when compared with that of conventional copper wire. © 2010 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 37B: 75–85, 2010
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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) | 0.000 | 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".