Cerebral perfusion metrics calculated directly from a hypoxia-induced step change in deoxyhemoglobin
Bibliographic record
Abstract
Resting cerebral perfusion metrics can be calculated from the MRI ΔR 2 * signal during the first passage of an intravascular bolus of a Gadolinium-based contrast agent (GBCA), or more recently, a transient hypoxia-induced change in the concentration of deoxyhemoglobin ([dOHb]). Conventional analysis follows a proxy process that includes deconvolution of an arterial input function (AIF) in a tracer kinetic model. We hypothesized that the step reduction in magnetic susceptibility accompanying a step decrease in [dOHb] that occurs when a single breath of oxygen terminates a brief episode of lung hypoxia permits direct calculation of relative perfusion metrics. The time course of the ΔR 2 * signal response enables both the discrimination of blood arrival times and the time course of voxel filling. We calculated the perfusion metrics implied by this step signal change in seven healthy volunteers and compared them to those from conventional analyses of GBCA and dOHb using their AIF and indicator dilution theory. Voxel-wise maps of relative cerebral blood flow and relative cerebral blood volume had a high spatial and magnitude congruence for all three analyses (r > 0.9) and were similar in appearance to published maps. The mean (SD) transit times (s) in grey and white matter respectively for the step response (7.4 (1.1), 8.05 (1.71)) were greater than those for GBCA (2.6 (0.45), 3.54 (0.83)) attributable to the nature of their respective calculation models. In conclusion we believe these calculations of perfusion metrics derived directly from ΔR 2 * have superior merit to calculations via AIF by virtue of being calculated from a direct signal rather than through a proxy model which encompasses errors inherent in designating an AIF and performing deconvolution calculations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".