Theoretical signal‐to‐noise ratio and spatial resolution dependence on the magnetic field strength for hyperpolarized noble gas magnetic resonance imaging of human lungs
Bibliographic record
Abstract
In hyperpolarized noble gas (HNG) magnetic resonance (MR) imaging, the available polarization is independent of magnetic field strength and for large radiofrequency (rf) coils, such as those used for chest imaging, the body noise becomes the primary noise source making signal-to-noise ratio (SNR) largely frequency independent at intermediate field strengths (0.1-0.5 T). Furthermore, the reduction in the transverse relaxation time, T2, of HNG in lungs with increasing field strength, results in a decrease in the achievable SNR at higher fields. In this work, the optimum field strength for HNG MR imaging was theoretically calculated in terms of both SNR and spatial resolution. SNR calculations used the principle of reciprocity and included contributions to the noise arising from both coil and sample losses in a chest-sized coil for lung imaging. The effects of susceptibility differences, transverse relaxation time, and diffusion were considered in the resolution calculations. The calculations show that the optimum field strength for HNG MR imaging of human lungs is between 0.1 and 0.6 T depending on gas type (helium or xenon) and sample size. At the field strengths currently used by conventional clinical proton MR imaging systems (1-3 T), the predicted SNR are 10%-50% lower than at the optimum field with only slightly worse spatial resolution (10%-20%). At higher fields (>3 T), however, the SNR degrades considerably reducing the achievable spatial resolution. Although HNG of the lung is still feasible at very low field strengths (<50 mT), the available SNR is much lower than at optimum fields and this reduces the achievable spatial resolution. These findings suggest that HNG imaging may be optimally performed at much lower field strengths (0.1-0.6 T) than conventional clinical proton MR imaging systems. This could considerably decrease cost, improve patient access, and reduce chemical shift and susceptibility artifacts and rf heating.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".