Hyperpolarized 129Xe Time-of-Flight MR Imaging of Perfusion and Brain Function
Bibliographic record
Abstract
Perfusion measurements can provide vital information about the homeostasis of an organ and can therefore be used as biomarkers to diagnose a variety of cardiovascular, renal, and neurological diseases. Currently, the most common techniques to measure perfusion are 15O positron emission tomography (PET), xenon-enhanced computed tomography (CT), single photon emission computed tomography (SPECT), dynamic contrast enhanced (DCE) MRI, and arterial spin labeling (ASL) MRI. Here, we show how regional perfusion can be quantitively measured with magnetic resonance imaging (MRI) using time-resolved depolarization of hyperpolarized (HP) xenon-129 (129Xe), and the application of this approach to detect changes in cerebral blood flow (CBF) due to a hemodynamic response in response to brain stimuli. The investigated HP 129Xe Time-of-Flight (TOF) technique produced perfusion images with an average signal-to-noise ratio (SNR) of 10.35. Furthermore, to our knowledge, the first hemodynamic response (HDR) map was acquired in healthy volunteers using the HP 129Xe TOF imaging. Responses to visual and motor stimuli were observed. The acquired HP TOF HDR maps correlated well with traditional proton blood oxygenation level-dependent functional MRI. Overall, this study expands the field of HP MRI with a novel dynamic imaging technique suitable for rapid and quantitative perfusion imaging.
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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 | 0.000 | 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 | 0.000 | 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".