Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI

Fuente: arXiv
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Auteurs principaux: Borghouts, Marijn, McKinley, Richard, Köstner, Manuel, Pluim, Josien, Wiest, Roland, Su, Ruisheng
Format: Preprint
Publié: 2025
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author Borghouts, Marijn
McKinley, Richard
Köstner, Manuel
Pluim, Josien
Wiest, Roland
Su, Ruisheng
author_facet Borghouts, Marijn
McKinley, Richard
Köstner, Manuel
Pluim, Josien
Wiest, Roland
Su, Ruisheng
contents Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge. While computed tomography (CT) based protocols are commonly used in emergency settings, their sensitivity for detecting distal occlusions is limited. This study explores the potential of magnetic resonance perfusion (MRP) imaging as a tool for differentiating distal AIS from epileptic seizures, a prevalent SM. Using a retrospective dataset of 162 patients (129 AIS, 33 seizures), we extracted region-wise perfusion map descriptors (PMDs) from dynamic susceptibility contrast (DSC) images. Statistical analyses identified several brain regions, located mainly in the temporal and occipital lobe, exhibiting significant group differences in certain PMDs. Hemispheric asymmetry analyses further highlighted these regions as discriminative. A logistic regression model trained on PMDs achieved an area under the receiver operating characteristic (AUROC) curve of 0.90, and an area under the precision recall curve (AUPRC) of 0.74, with a specificity of 92% and a sensitivity of 73%, suggesting strong performance in distinguishing distal AIS from seizures. These findings support further exploration of MRP-based PMDs as interpretable features for distinguishing true strokes from various mimics. The code is openly available at our GitHub https://github.com/Marijn311/PMD_extraction_and_analysis{github.com/Marijn311/PMD\_extraction\_and\_analysis
format Preprint
id arxiv_https___arxiv_org_abs_2508_04404
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
Borghouts, Marijn
McKinley, Richard
Köstner, Manuel
Pluim, Josien
Wiest, Roland
Su, Ruisheng
Image and Video Processing
Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge. While computed tomography (CT) based protocols are commonly used in emergency settings, their sensitivity for detecting distal occlusions is limited. This study explores the potential of magnetic resonance perfusion (MRP) imaging as a tool for differentiating distal AIS from epileptic seizures, a prevalent SM. Using a retrospective dataset of 162 patients (129 AIS, 33 seizures), we extracted region-wise perfusion map descriptors (PMDs) from dynamic susceptibility contrast (DSC) images. Statistical analyses identified several brain regions, located mainly in the temporal and occipital lobe, exhibiting significant group differences in certain PMDs. Hemispheric asymmetry analyses further highlighted these regions as discriminative. A logistic regression model trained on PMDs achieved an area under the receiver operating characteristic (AUROC) curve of 0.90, and an area under the precision recall curve (AUPRC) of 0.74, with a specificity of 92% and a sensitivity of 73%, suggesting strong performance in distinguishing distal AIS from seizures. These findings support further exploration of MRP-based PMDs as interpretable features for distinguishing true strokes from various mimics. The code is openly available at our GitHub https://github.com/Marijn311/PMD_extraction_and_analysis{github.com/Marijn311/PMD\_extraction\_and\_analysis
title Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
topic Image and Video Processing
url https://arxiv.org/abs/2508.04404