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Related Experiment Video

Updated: Aug 18, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Predicting final ischemic stroke lesions from initial diffusion-weighted images using a deep neural network.

Sanaz Nazari-Farsani1, Yannan Yu2, Rui Duarte Armindo3

  • 1Department of Radiology, Stanford University, CA, USA.

Neuroimage. Clinical
|December 8, 2022
PubMed
Summary
This summary is machine-generated.

A deep convolutional neural network (DCNN) model can predict final stroke infarct volume using only admission diffusion-weighted imaging. This AI approach may streamline acute stroke imaging protocols, aiding faster treatment decisions.

Keywords:
Acute ischemic strokeDWIDeep learningLesion segmentationMRIPWI

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Stroke Neurology

Background:

  • Diffusion-perfusion mismatch is crucial for estimating stroke infarct risk without reperfusion.
  • Perfusion-weighted imaging (PWI) prolongs and increases the cost of acute stroke workups.
  • Exploring AI to predict stroke outcomes using limited imaging data is essential.

Purpose of the Study:

  • To determine if a deep convolutional neural network (DCNN) trained on diffusion-weighted imaging (DWI) can predict final infarct volume and location.
  • To assess the feasibility of reducing acute stroke imaging time and cost.
  • To develop a predictive model for stroke prognosis using readily available admission imaging.

Main Methods:

  • An attention-gated DCNN (AG-DCNN) was trained and validated on 445 acute stroke patients.
  • Input data included DWI, apparent diffusion coefficient (ADC) maps, and thresholded ADC maps.
  • The model predicted voxel-wise infarction probability maps, with performance evaluated using AUC, DSC, and volume error.

Main Results:

  • The AG-DCNN achieved a median AUC of 0.91.
  • Median sensitivity and specificity for infarction prediction were 0.60 and 0.97, respectively.
  • The model demonstrated good correlation (ρc = 0.73) between predicted and true infarct volumes.

Conclusions:

  • An AG-DCNN utilizing only admission DWI can predict 3-7 day infarct volumes accurately.
  • This AI model shows comparable accuracy to methods using both DWI and PWI.
  • The findings suggest potential for shorter stroke imaging protocols, facilitating quicker treatment decisions.