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

TL;DR

This study demonstrates that dynamic susceptibility contrast MRI perfusion map descriptors can effectively differentiate distal ischemic stroke from seizure-induced stroke mimics, achieving high accuracy and potential for clinical application.

Contribution

The paper introduces a novel approach using perfusion map descriptors from DSC MRI to distinguish distal AIS from seizures, with a validated logistic regression model showing high diagnostic performance.

Findings

Logistic regression model achieved AUROC of 0.90.

Regions mainly in temporal and occipital lobes showed significant differences.

Model specificity of 92% and sensitivity of 73%.

Abstract

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