The value of 3D pseudo-continuousarterial spin labeling perfusion imaging in moyamoya disease—Comparison with dynamic susceptibility contrast perfusion imaging
Bibliographic record
Abstract
Background and purpose 3D pseudo-continuous arterial spin labeling (3D pCASL) is commonly used to measure arterial cerebral blood flow (CBF). The aim of this study was to assess the clinical feasibility and accuracy of 3D pCASL in comparison with dynamic susceptibility contrast (DSC) perfusion imaging in moyamoya disease (MMD). Materials and methods A total of 174 MMD patients underwent 3D pCASL and DSC-MRI for evaluating cerebral blood perfusion. 3D-pCASL with two single post-labeling delay (PLD) times (1,500 and 2,500 ms) was used to measure CBF. The values of DSC-CBF and ASL-CBF were calculated for major arterial territories including the anterior, middle, and posterior cerebral arteries as well as the areas based on the Alberta Stroke Program Early CT Score (ASPECTS) template. The correlation between DSC-CBF and ASL-CBF was analyzed. The consistency and accuracy between the two methods in assessing the cerebral ischemic state before and after surgery were analyzed. Results The correlation between ASL (2,500 ms) and DSC-MRI was slightly better than the correlation between ASL (1,500 ms) and DSC-MRI in major vascular territories before revascularization. Significant correlations were observed between ASL (2,500 ms) and DSC-MRI and between ASL (1,500 ms) and DSC-MRI in major vascular territories after revascularization. For 44 surgically treated patients, the scores of ASPECTS for CBF on the operated side were significantly different before and after revascularization (p < 0.05) and showed good consistency on all the examination methods. A comparison of the scores of ASPECTS of the three parameters before and after revascularization showed that there was no statistical difference between them (p > 0.05). Conclusion Compared to DSC-MRI, 3D pCASL can assess the cerebral blood perfusion in MMD before and after revascularization effectively. 3D pCASL showed the feasibility and clinical utility value in patients with MMD.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".