Cerebral perfusion metrics calculated directly, model-free, from a hypoxia-induced step change in deoxyhemoglobin
Bibliographic record
Abstract
ABSTRACT Dynamic susceptibility contrast (DSC) perfusion MRI measures blood flow metrics via application of a BOLD pulse sequence during transit of a gadolinium-based contrast agent (GBCA), We have previously shown that can also be performed using endogenous deoxyhemoglobin (dOHb) as a replacement for GBCA since dOHb also has the necessary paramagnetic properties similar to GBCA 1 . Conventional analysis with these contrast agents includes deconvolution of an arterial input function (AIF) assuming a kinetic model. However, the immediate decrease in dOHb that occurs during reoxygenation from a transient hypoxia constitutes a step reduction in susceptibility that permits direct, model-free, calculation of relative resting perfusion metrics. The metrics from this step analysis for seven healthy volunteers were compared to those from a conventional analysis of GBCA and dOHb. Voxel- wise maps of mean transit time, and relative cerebral blood flow and cerebral blood volume, had a high spatial congruence for all three analyses and were similar in appearance to published maps. The time course of the T2*-weighted signal step response identifies both the arrival time, the distribution of the step increase in arterial oxygen saturation, SaO 2 , and the voxel filling phase. The analysis of a step change in dOHb during rapid reoxygenation is an alternative way of calculating perfusion metrics directly from measurements of the resulting T2*-weighted signal, avoiding the potential errors from the use of a kinetic model and requirement for selection of an AIF.
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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 | 0.001 |
| 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".