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Deep learning effectively corrects background phase error in four-dimensional (4D) flow MRI for abdominopelvic vascular imaging. Automated correction achieved results comparable to manual methods, improving flow measurement accuracy.

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Area of Science:

  • Medical Imaging
  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine

Background:

  • Four-dimensional (4D) flow MRI offers hemodynamic insights for abdominopelvic vascular diseases.
  • Clinical utility is limited by challenging background phase error correction.

Purpose of the Study:

  • To evaluate deep learning for automated background phase error correction in 4D flow MRI.
  • To compare automated correction with manual image-based correction.

Main Methods:

  • Retrospective analysis of 139 abdominopelvic 4D flow MRI acquisitions.
  • Training a multichannel 3D U-Net convolutional neural network on 86% of data.
  • Comparison of automated correction with manual correction using Pearson correlation and Bland-Altman analysis.

Main Results:

  • Automated correction showed strong correlation with manual correction (ρ = 0.98, P < .001).
  • Both methods significantly reduced inflow-outflow variance, improving mean difference.
  • No significant difference in inflow-outflow variance between manual and automated correction (P = .10).

Conclusions:

  • Deep learning provides feasible and effective automated background phase error correction in 4D flow MRI.
  • Automated correction yields results comparable to manual correction, enhancing flow measurement reliability.