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Detecting Basilar Artery Wall Pathology in Isolated Pontine Infarction with Short-TE SWI Magnitude Images

Liu, H.-M.; Huang, A.; Yung-Chuan, H.; Wu, C.-H.; Hsu, L.-S.; Yang, N.; Lin, K.-C.

2025-09-25 radiology and imaging
10.1101/2025.09.23.25336518 medRxiv
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Background and PurposeIntracranial atherosclerosis is a significant contributor to ischemic stroke, often through plaques without luminal stenosis. Current imaging techniques struggle to detect vessel wall pathology effectively. This study evaluates short-TE susceptibility-weighted imaging magnitude images (STE-MI) for identifying cerebral microbleeds and basilar artery (BA) wall changes in acute isolated pontine infarction, with reduced artifacts compared to SWAN. MethodsIn this post hoc analysis, 2,475 brain MR exams conducted between March 2022 and March 2025 using a 3.0 T scanner compared STE-MI (TE=7.2 ms) with SWAN for microbleed detection and BA artifact reduction. Among 131 patients with acute posterior circulation stroke (January 2023-April 2025), vessel wall pathology was assessed in 92 patients with vertebrobasilar stenosis (VBASO) and 39 with non-stenotic non-cardioembolic (NSNCE) stroke using STE-MI. Interobserver agreement and comparative statistics were analyzed. ResultsSTE-MI detected 86% of microbleeds identified by SWAN in 33 subjects (Cohens kappa=0.828 for interobserver agreement). Blooming artifacts around the BA were reduced in 96.9% of 2,475 cases with STE-MI compared to 5.5% with SWAN. In the NSNCE group, 10 of 16 patients with isolated pontine infarction exhibited extraluminal BA pathology on STE-MI (mixed hypo-/intermediate intensity in 6, hypointensity in 4), correlating with diffusion-weighted imaging (DWI) lesions. ConclusionsSTE-MI effectively minimizes artifacts and detects BA wall pathology in acute isolated pontine infarction, particularly in NSNCE stroke. This approach may enhance the identification of culprit lesions, potentially improving risk stratification and guiding targeted therapies.

Published in Stroke: Vascular and Interventional Neurology (predicted rank #3) · training set

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