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A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging.

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Heliyon
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A new deep learning framework accurately positions brain regions using functional ultrasound images. This method relies on vascular patterns, offering reliable navigation for neuroimaging research even in challenging conditions like stroke.

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Accurate positioning and navigation are critical for high-quality neuroimaging data acquisition.
  • Functional ultrasound (fUS) imaging offers high-resolution brain vasculature visualization for monitoring brain activity.
  • A standardized method for inferring brain position from fUS vascular images is currently lacking.

Purpose of the Study:

  • To develop and evaluate a deep learning-based framework for precise positioning using fUS brain images.
  • To enable reliable navigation and data interpretation in functional ultrasound imaging studies.

Main Methods:

  • A deep learning framework employing an image classification task combined with probability regression was developed.
  • The framework was trained and evaluated on a dataset of 51 rat brain scans.
  • GradCAM analysis was used to interpret the model's decision-making process, focusing on vascular structures.

Main Results:

  • The developed framework achieved a positioning error of 176 μm with training positions at 375 μm intervals.
  • GradCAM analysis indicated that predictions were predominantly based on subcortical vascular patterns.
  • The method demonstrated robustness and reliability in challenging conditions, such as impaired vasculature in a cortical stroke model, with no significant increase in misclassifications.

Conclusions:

  • The proposed deep learning framework provides accurate and flexible positioning for functional ultrasound imaging.
  • The method leverages conserved vascular patterns, eliminating the need for pre-registered references.
  • This approach enhances the reliability of neuroimaging by enabling precise localization within the brain vasculature.