Preclinical hyperpolarized <sup>129</sup>Xe MRI: ventilation and <i>T</i><sub>2</sub>* mapping in mouse lungs at 7 T using multi‐echo flyback UTE
Bibliographic record
Abstract
Fast apparent transverse relaxation (short T2*) is a common obstacle when attempting to perform quantitative 1H MRI of the lungs. While T2* times are longer for pulmonary hyperpolarized (HP) gas functional imaging (in particular for gaseous 129Xe), T2* can still lead to quantitative inaccuracies for sequences requiring longer echo times (such as diffusion weighted images) or longer readout duration (such as spiral sequences). This is especially true in preclinical studies, where high magnetic fields lead to shorter relaxation times than are typically seen in human studies. However, the T2* of HP 129Xe in the most common animal model of human disease (mice) has not been reported. Herein, we present a multi‐echo radial flyback imaging sequence and use it to measure HP 129Xe T2* at 7 T under a variety of respiratory conditions. This sequence mitigates the impact of T1 relaxation outside the animal by using multiple gradient‐refocused echoes to acquire images at a number of effective echo times for each RF excitation. After validating the sequence using a phantom containing water doped with superparamagnetic iron oxide nanoparticles, we measured the 129Xe T2* in vivo for 10 healthy C57Bl/6 J mice and found T2* ~ 5 ms in the lung airspaces. Interestingly, T2* was relatively constant over all experimental conditions, and varied significantly with sex, but not age, mass, or the O2 content of the inhaled gas mixture. These results are discussed in the context of T2* relaxation within porous media.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".