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Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network.

Euijin Jung1, Philip Chikontwe1, Xiaopeng Zong2

  • 1Department of Robotics Engineering, DGIST, Daegu 42988, South Korea.

IEEE Access : Practical Innovations, Open Solutions
|April 9, 2019
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Summary
This summary is machine-generated.

This study introduces a deep learning method to enhance brain MRI scans, improving visualization of perivascular spaces (PVS) for better disease detection. The novel 3D convolutional neural network significantly outperforms existing techniques.

Keywords:
MRI enhancementPerivascular spacesdeep convolutional neural networkdensely connected networkskip connections

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Perivascular spaces (PVS) are implicated in various brain diseases.
  • Quantifying PVS is challenging due to their indistinct appearance in MR images.

Purpose of the Study:

  • To develop a deep learning method for enhancing Magnetic Resonance (MR) images to improve PVS visualization.
  • To introduce a novel, very deep 3D convolutional neural network for accurate PVS enhancement.

Main Methods:

  • A very deep 3D convolutional neural network with dense connections and skip connections was proposed.
  • The network leverages rich contextual information from multi-level features to mitigate gradient vanishing.
  • The method was evaluated using 17 7T MR images with twofold cross-validation.

Main Results:

  • The proposed deep learning method significantly enhances PVS visualization in MR images.
  • The network effectively utilizes contextual information for accurate image prediction.
  • Experimental results demonstrate superior performance compared to previous PVS enhancement methods.

Conclusions:

  • The developed deep learning approach offers a more effective way to visualize PVS in brain MR images.
  • This method has the potential to improve the diagnosis and study of PVS-related brain diseases.
  • The proposed 3D convolutional neural network architecture is highly effective for medical image enhancement.