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Referenceless 4D flow cardiovascular magnetic resonance with deep learning.

Chiara Trenti1, Erik Ylipää2, Tino Ebbers3

  • 1Department of Health, Medicine and Caring Sciences (HMV), Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden.

Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance
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PubMed
Summary

Deep learning significantly reduces scan times for four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) by predicting reference encoding. This enables faster, high-resolution cardiovascular assessments in clinical practice.

Keywords:
4D flowAccelerated techniquesCardiovascular imagingDeep learningPhase-contrast

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Area of Science:

  • Cardiovascular Imaging
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is crucial for assessing cardiovascular diseases but is limited by lengthy acquisition times.
  • Conventional 4D flow CMR requires a reference encoding scan, contributing to extended scan durations.
  • Reducing scan time is essential for improving the clinical utility and patient experience of 4D flow CMR.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting the reference encoding in 4D flow CMR.
  • To enable referenceless 4D flow CMR acquisition, thereby reducing scan time and potentially improving image resolution.
  • To assess the accuracy of deep learning-based velocity and flow quantification compared to conventional methods.

Main Methods:

  • A U-Net deep learning architecture was trained using adversarial learning (U-NetADV) and a velocity frequency-weighted loss function (U-NetVEL).
  • The models predicted reference encoding from three motion encodings in whole-heart 4D flow datasets from 126 patients.
  • Quantitative assessments included flow volumes, velocities, and turbulent kinetic energy in major cardiac structures.

Main Results:

  • Deep learning-predicted reference encodings yielded 3D velocity data comparable to scanner-acquired data.
  • U-NetADV demonstrated consistent performance across the cardiac cycle and subjects, while U-NetVEL excelled in systolic velocity prediction.
  • Quantification errors for flow volumes and velocities were generally low, with specific exceptions in turbulent kinetic energy calculations.

Conclusions:

  • Deep learning-based referenceless 4D flow CMR provides accurate quantification of velocities and flow volumes.
  • Eliminating the reference scan reduces acquired data by 25%, allowing for shorter scan times or higher resolution.
  • This advancement holds significant potential for routine clinical application of 4D flow CMR.