Improving signal‐to‐noise ratio of hyperpolarized noble gas MR imaging at 73.5 mT using multiturn Litz wire radiofrequency receive coils
Bibliographic record
Abstract
Abstract Hyperpolarized noble gases (3He, 129Xe) are used in Magnetic Resonance imaging as inhaled contrast agents to visualize the lung. Because the magnetization derived from hyperpolarization is independent of magnetic field strength, low magnetic fields can be used, where coil noise dominates over sample noise. It has been shown previously that signal‐to‐noise ratio (SNR) of hyperpolarized 129Xe rat lung images can be improved with the use of saddle‐shaped radiofrequency receive coils constructed of Litz wire in a single layer with up to 20 turns. Increased number of turns is expected to provide additional sensitivity. In this work, coils of the same geometry were built using Litz wire with either the addition of more turns (up to 48) or the addition of two layers and compared using water phantoms. It is shown that the addition of 48 turns in a single layer provided a 42% increase in SNR, when compared to the 20 turns, whereas the dual‐layered approach provided no measurable benefit. © 2011 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 39B:37–42, 2011
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".