Editorial: Measuring resting cerebral perfusion using magnetic resonance imaging (MRI)
Bibliographic record
Abstract
Measuring resting cerebral perfusion using magnetic resonance imaging (MRI) Cerebral perfusionCerebral perfusion metrics such as cerebral blood flow (CBF) provide an important assessment of cerebrovascular health and the hemodynamic consequences of cerebrovascular disease and ischemic events.For example, the detection of collateral blood circulation, which is widely recognised as a crucial protective mechanism that can significantly influence clinical outcomes following ischemic events such as stroke.Moreover, understanding cerebral blood flow distribution patterns can aid in surgical planning for interventional procedures, and assessment of their effectiveness.These considerations underscore the necessity for developing MRI-based techniques to measure perfusion metrics together with their associated post-processing methods.An advantage of MRI is the ability to deliver volumetric, temporally resolved information to which classical methods such as transcranial doppler are insensitive as Fico et al. explain.They demonstrated this aspect, showing that 4D flow MRI was more sensitive to age-related differences in cerebrovascular reactivity when comparing with analogous velocity-based measurements made using TCD.This was despite good agreement between middle cerebral artery velocity measurements as measured using each technique.The reproducibility of MRI measurements is an important consideration when examining longitudinal changes in perfusion metrics, as Madsen et al. remind us.They examined the longitudinal reproducibility of a selection of metabolic (lactate and N-acetylaspartate (NAA) concentrations) and hemodynamic (cerebral blood flow, cerebral metabolic rate of oxygen consumption and global arterio-venous oxygen saturation difference) MR parameters.With the exception of lactate, MR parameters showed good within-day reproducibility, which declined between 7 days and several week measurements.Understanding physiological variability is crucial for optimizing study designs and establishing the sample sizes required to adequately power large-scale studies.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.026 | 0.029 |
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".