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StrokeDiffNet: quantifying DWI-FLAIR mismatch via a common feature space for time since stroke classification.

Jianing Li1, Zhihao Lin2, Yu Xin3

  • 1The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, PR China.

Medical & Biological Engineering & Computing
|May 28, 2026
PubMed
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This summary is machine-generated.

StrokeDiffNet accurately estimates time since stroke onset (TSS) in acute ischemic stroke (AIS) by mapping DWI and FLAIR images into a common feature space. This novel approach improves TSS classification performance for better treatment decisions.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Time since stroke onset (TSS) is critical for acute ischemic stroke (AIS) treatment.
  • Current DWI-FLAIR mismatch methods struggle with modality differences and feature alignment for precise TSS estimation.
  • Accurate TSS quantification is essential for effective clinical decision-making in AIS.

Purpose of the Study:

  • To develop a novel deep learning model, StrokeDiffNet, for accurate TSS estimation in AIS.
  • To address the challenges of modality-style differences and feature misalignment in DWI and FLAIR imaging.
  • To improve the quantitative measurement of inter-modality differences for enhanced TSS classification.

Main Methods:

  • Proposed StrokeDiffNet to map DWI and FLAIR features into a common feature space (CFS).
Keywords:
Acute ischemic strokeContrastive learningCross-domain mixup noiseDWI-FLAIR mismatchFeature alignmentTime since stroke

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  • Employed self-reconstruction and interactive supervision for DWI-FLAIR encoder training to promote feature alignment.
  • Introduced a cross-domain mixed-noise strategy for self-supervised training and a key-feature alignment strategy.

    • Main Results:

    • StrokeDiffNet achieved strong TSS classification performance.
    • Achieved accuracy of 73.0%, precision of 78.3%, F1-score of 78.3%, and AUC of 71.3%.
    • Outperformed other mainstream classification networks in TSS classification tasks.

    Conclusions:

    • StrokeDiffNet effectively overcomes modality-style interference and improves key feature alignment for TSS estimation.
    • The proposed method offers a promising approach for quantitative mismatch measurement and TSS classification in AIS.
    • StrokeDiffNet demonstrates significant potential for improving clinical decision-making in acute ischemic stroke management.