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Deep learning phase error correction for cerebrovascular 4D flow MRI.

Shanmukha Srinivas1,2, Evan Masutani1, Alexander Norbash1

  • 1Department of Radiology, University of California San Diego, 200 West Arbor Drive, San Diego, CA, 92103, USA.

Scientific Reports
|June 5, 2023
PubMed
Summary
This summary is machine-generated.

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Background phase errors in 4D Flow MRI impact blood flow quantification. A deep learning convolutional neural network (CNN) fully automates correction, matching manual accuracy for cerebrovascular flow measurements.

Area of Science:

  • Medical Imaging
  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine

Background:

  • Background phase errors in 4D Flow MRI can compromise cerebrovascular blood flow quantification.
  • Accurate flow measurements are crucial for assessing conditions affecting the brain's vascular system.

Purpose of the Study:

  • To evaluate the impact of background phase errors on cerebrovascular flow volume measurements.
  • To assess the effectiveness of manual, image-based correction for these errors.
  • To investigate the potential of a convolutional neural network (CNN) for automated phase error correction.

Main Methods:

  • Retrospective analysis of 96 4D Flow MRI exams from 48 patients.
  • Flow measurements of anterior, posterior, and venous circulation were performed.

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  • A CNN was trained to directly infer phase error correction fields from 4D Flow data.

    • Main Results:

    • Manual correction significantly improved inflow-outflow correlation and decreased variance in flow measurements (p < 0.001).
    • Automated CNN correction demonstrated non-inferior performance compared to manual correction.
    • No significant differences were found in correlation or bias between manual and CNN correction methods.

    Conclusions:

    • Residual background phase errors can impair inflow-outflow consistency in cerebrovascular flow measurements.
    • A CNN can effectively automate phase error correction, achieving results comparable to manual methods.
    • Deep learning offers a promising approach for accurate and efficient 4D Flow MRI analysis.