Monitoring Cerebral Perfusion Changes after Revascularization in Patients with Moyamoya Disease by Using Arterial Spin-labeling MR Imaging
Bibliographic record
Abstract
Purpose To determine whether arterial spin-labeling (ASL) magnetic resonance (MR) imaging could be used to identify changes in cerebral blood flow (CBF), collateral blood flow, and anastomosis site patency after revascularization in patients with moyamoya disease. Materials and Methods This retrospective study was conducted in 145 patients with moyamoya disease who underwent middle cerebral artery (MCA)–superficial temporal artery anastomosis. Preoperative, early postoperative, and late postoperative ASL and digital subtraction angiography images were analyzed. In the MCA territory, absolute CBF (hereafter, CBFMCA) and normalized CBF values adjusted to nonanastomosis side (hereafter, nCBFMCA) and to cerebellum (hereafter, nCBFCbll) were calculated. Collateral grading in the MCA territory was assessed according to Alberta Stroke Program Early CT Score methodology, and anastomosis site patency were also assessed. Changes in CBF were compared by using one-way analysis of variance with Bonferroni correction for multiple comparisons. Intermodality agreement was determined by κ statistics. Results Significant increases in CBFMCA, nCBFMCA, and nCBFCbll were found after revascularization (preoperative and postoperative values of CBFMCA, 35.2 mL/100 g per minute ± 7.8 [mean ± standard deviation] and 51.5 mL/100 g per minute ± 12.0; nCBFMCA, 0.73 mL/100 g per minute ± 0.14 and 1.01 mL/100 g per minute ± 0.18; nCBFCbll, 0.74 mL/100 g per minute ± 0.12 and 1.12 mL/100 g per minute ± 0.16; all P < .001). Agreements for collateral grading and anastomosis patency between ASL MR imaging and digital subtraction angiography were moderate to good, with weighted κ values of 0.77 (95% confidence interval: 0.73, 0.81) and 0.57 (95% confidence interval: 0.37, 0.76), respectively. Conclusion ASL MR imaging can be used to identify perfusion changes in patients with moyamoya disease after revascularization as a noninvasive monitoring tool. © RSNA, 2018 Online supplemental material is available for this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".