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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Automated DWI-FLAIR mismatch assessment in stroke using DWI only.

Joseph Benzakoun^1,2,3, Lauranne Scheldeman^3,4, Anke Wouters^3,4

¹IMA-BRAIN, INSERM U1266, Institute of Psychiatry and Neuroscience of Paris (IPNP), Université Paris Cité, Paris, France.

European Stroke Journal

|August 28, 2025

Summary

This summary is machine-generated.

A deep-learning model accurately predicts Diffusion-Weighted Imaging-Fluid-Attenuated Inversion-Recovery mismatch in Acute Ischemic Stroke patients using only DWI data. This tool aids in identifying candidates for thrombolysis when stroke onset is unknown.

Keywords:

Ischemic stroke artificial intelligence decision support techniques diffusion magnetic resonance imaging magnetic resonance imaging

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Area of Science:

Radiology
Artificial Intelligence
Neurology

Background:

Diffusion-Weighted Imaging (DWI) and Fluid-Attenuated Inversion-Recovery (FLAIR) mismatch is crucial for identifying Acute Ischemic Stroke (AIS) patients eligible for thrombolysis, especially when stroke onset is unknown.
Visual assessment of DWI-FLAIR mismatch has limitations due to suboptimal observer agreement, impacting treatment decisions for approximately 15% of AIS cases.

Purpose of the Study:

To develop and validate a Deep-Learning (DL) model capable of predicting DWI-FLAIR mismatch using only DWI data.
To improve the accuracy and consistency of DWI-FLAIR mismatch identification in AIS patients.

Main Methods:

A retrospective study utilizing AIS patient data from the ETIS registry (derivation) and WAKE-UP trial (validation).
A DL model was trained to predict FLAIR Visible Areas (FVA) using only DWI input, defining an FVA-index.
Model performance was evaluated using Area Under the ROC Curve (AUC) and optimal FVA-index cutoff for predicting DWI-FLAIR mismatch.

Main Results:

The DL model demonstrated strong predictive value in both derivation (AUC=0.85) and validation cohorts (AUC=0.86).
An optimal FVA-index cutoff of 0.5 achieved 70% sensitivity and 88% specificity in the validation cohort.
The model showed good agreement with visual ratings, indicated by a kappa of 0.54.

Conclusions:

The developed DL model accurately predicts DWI-FLAIR mismatch in AIS patients with unknown stroke onset.
This AI tool can assist clinicians in challenging visual assessments or when FLAIR sequences are unavailable, potentially optimizing thrombolysis decisions.