Abstract
Purpose: To evaluate performance of synthetic and real FLAIR for identifying early stroke in a multicenter
cohort.
Methods: This retrospective study was conducted using DWI and FLAIR extracted from the Endovascular Treatment in
Ischemic Stroke image registry (2017–2021). The database was partitioned into subsets according to MRI field strength
and manufacturer, and randomly divided into training set (70%) used for model fine-tuning, validation set (15%), and
test set (15%). In test set, five readers, blinded to FLAIR sequence type, assessed DWI-FLAIR mismatch using real and
synthetic FLAIR. Interobserver agreement for DWI-FLAIR rating and concordance between synthetic and real FLAIR
were evaluated with kappa statistics. Sensitivity and specificity for identification of ⩽4.5 h AIS were compared in patients
with known onset-to-MRI delay using McNemar’s test.

Conclusion: A deep-learning-based FLAIR fine-tuned on multicenter data can provide comparable performances to real
FLAIR for early AIS identification. This approach may help reducing MR protocol duration and motion artifacts.