Neuroradiology
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Regional brain volume estimation in multiple sclerosis (MS) patients is prone to error due to white matter lesions being erroneously segmented as grey matter. The Lesion Segmentation Toolbox (LST) is an automatic tool that estimates a lesion mask based on 3D T2-FLAIR images and then uses this mask to fill the structural MRI image. The goal of this study was (1) to test the LST for estimating white matter lesion volume in a cohort of MS patients using 2D T2-FLAIR images, and (2) to evaluate the performance of the optimized LST on image segmentation and the impact on the calculated grey matter fraction (GMF). ⋯ LST lesion volume calculation seems reliable. GMFs are significantly reduced when a method to correct the contribution of MS lesions is used, and it may have an impact in assessing GMF differences between clinical cohorts.
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Reliable predictors of poor clinical outcome despite successful revascularization might help select patients with acute ischemic stroke for thrombectomy. We sought to determine whether baseline Alberta Stroke Program Early CT Score (ASPECTS) applied to CT angiography source images (CTA-SI) is useful in predicting futile recanalization. ⋯ CTA-SI-ASPECTS strongly predicts futile recanalization and could be a valuable tool for treatment decisions regarding the indication of revascularization therapies.