Quantification of Cerebrovascular Reactivity by Blood Oxygen Level–Dependent MR Imaging and Correlation with Conventional Angiography in Patients with Moyamoya Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: BOLD MR imaging combined with a technique for precision control of end-tidal pCO2 was used to produce quantitative maps of CVR in patients with Moyamoya disease. The technique was validated against measures of disease severity by using conventional angiography; it then was used to study the relationship between CVR, vascular steal, and disease severity. MATERIALS AND METHODS: A retrospective analysis comparing conventional angiography with BOLD MR imaging was performed on 11 patients with Moyamoya disease. Iso-oxic cycling of end-tidal pCO2 between 2 target values was performed during BOLD MR imaging. CVR was calculated as the BOLD signal difference per ΔpCO2. CVR was correlated with the presence of Moyamoya or pial collaterals and the degree of Moyamaya disease as graded by using a modified Suzuki score. RESULTS: A good correlation between mean CVR and Suzuki score was found for the MCA and ACA territories (Pearson correlation coefficient, −0.7560 and −0.6140, respectively; P < .0001). A similar correlation was found between mean CVR and the presence of pial and Moyamoya collateral vessels for combined MCA and ACA territories (Pearson correlation coefficient, −0.7466; P < .0001). On a voxel-for-voxel basis, there was a greater extent of steal within vascular territories with increasing disease severity (higher modified Suzuki score). Mean CVR was found to scale nonlinearly with the extent of vascular steal. CONCLUSIONS: Quantitative measures of CVR show direct correlation with impaired vascular supply as measured by the modified Suzuki score and enable direct investigation of the physiology of autoregulatory reserve, including steal phenomenon, within a given vascular territory. ACA : anterior cerebral artery AFNI : analysis of functional neuroimages AIF : arterial input function ANOVA : analysis of variance BOLD : blood oxygen level−dependent CBF : cerebral blood flow CVR : cerebrovascular reactivity CO2 : carbon dioxide ΔSpCO2 : difference per change in end-title carbon dioxide f : fraction ICA : internal carotid artery MCA : middle cerebral artery neg : negative PCA : posterior cerebral artery pCO2 : partial pressure of carbon dioxide PET : positron-emission tomography pO2 : partial pressure of oxygen pos : positive SE : standard error SPECT : single-photon emission CT TIA : transient ischemic attack
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".