Poster — Thur Eve — 24: Variable Field Strength MR System for Hyperpolarized Noble Gas Imaging of Rodent Lungs
Bibliographic record
Abstract
The optimum field strength for hyperpolarized noble gas lung (129Xe and 3He) imaging depends on the sample/coil size and field‐dependence of the lung properties (i.e. relaxation times, susceptibility effects), and has been predicted to correspond to low field strengths (0.05–0.2T) for clinical studies. For the case of the small coils typically used for rodent lung imaging, it has been previously shown that the maximum SNR is expected to be at high fields (>3T). However, the SNR advantage of higher fields for rodent lung imaging can potentially be offset by improved coil design using Litz wire coils resulting in comparable SNR at low fields. In this work, a broad‐band variable field (0.01–0.15T) MR system is described, as well as experiments to validate the hyperpolarized noble gas image SNR dependence on field strength. The coil performance at 0.866MHz and 2.385MHz, corresponding to 129Xe and 3He frequencies at 73.5mT respectively, was optimized using multi‐turn Litz wire coils. The 129Xe and 3He image SNR of rat lung was investigated theoretically and in vivo, at 73.5mT and compared to images obtained at 3 T. Due to the improvement provided by the multi‐turn Litz coils, the SNR at 3T was predicted to be a factor of two higher compared to 73.5mT, this compares to a factor of about ten for single‐turned copper coils. In conclusion, hyperpolarized noble gas lung image quality in rats is comparable between 73.5mT and 3T, with the use of Litz wires together with the long T2* available at low magnetic field strength.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".