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Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds.

Vitus Gosch1, Kersten Villringer1, Ivana Galinovic1

  • 1Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Frontiers in Neurology
|August 14, 2023
PubMed
Summary
This summary is machine-generated.

Automated apparent diffusion coefficient (ADC) thresholding for acute ischemic stroke lesions shows variable performance. Lesion size and ADC values influence accuracy, indicating limitations for small lesions.

Keywords:
MRI analysis softwareacute ischemic strokeapparent diffusion coefficientautomated lesion delineationdiffusion-weighted imaging

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Area of Science:

  • Neuroimaging
  • Radiology
  • Stroke Medicine

Background:

  • Automated lesion segmentation is crucial in acute ischemic stroke MRI analysis.
  • Apparent Diffusion Coefficient (ADC) thresholding is a common technique for delineating diffusion-weighted imaging (DWI) lesions.

Purpose of the Study:

  • To evaluate the performance of ADC thresholding for segmenting baseline DWI lesions in acute ischemic stroke.
  • To identify patient-specific factors influencing the accuracy of automated ADC thresholding.

Main Methods:

  • Retrospective analysis of the 1000Plus study (NCT00715533).
  • Development of an automated segmentation algorithm using a fixed ADC threshold (≤620 × 10⁻⁶ mm²/s).
  • Comparison with manual segmentation, ROC analysis, and multiple linear regression to assess influencing factors.

Main Results:

  • The automated algorithm achieved a median Dice coefficient of 0.43.
  • Lesion volume and mean ADC values within the lesion significantly predicted performance (p < 0.001).
  • Optimal individual ADC thresholds varied, with mean lesion and parenchymal ADC values associated with performance.

Conclusions:

  • ADC thresholding performance for acute stroke lesions is patient-dependent.
  • Lesion size and ADC values (lesion and parenchyma) impact automated segmentation accuracy.
  • Automated delineation of very small lesions using ADC thresholding is unreliable due to map noise.