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Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification.

Yoon-Chul Kim1, Kyurae Kim2, Yeon Hyeon Choe3

  • 1Division of Digital Healthcare, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Republic of Korea.

Quantitative Imaging in Medicine and Surgery
|December 18, 2023
PubMed
Summary

A new automatic method using deep learning and machine learning accurately estimates myocardial perfusion reserve index (MPRI) from MRI scans. This automated approach offers efficient and quantitative assessment of myocardial ischemia without manual intervention.

Keywords:
Cardiac magnetic resonance imaging (cardiac MRI)deep learningmyocardial perfusion reserve index (MPRI)perfusionsegmentation

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Myocardial perfusion reserve index (MPRI) measured via MRI is crucial for detecting ischemia.
  • Current MPRI measurement techniques are often manual and time-consuming.
  • Developing automated methods is essential for efficient clinical application.

Purpose of the Study:

  • To develop a fully automatic method for estimating MPRI using deep learning and machine learning.
  • To evaluate the performance and accuracy of the proposed automated MPRI measurement technique.
  • To compare the automated method against a manual reference standard.

Main Methods:

  • Utilized Monte Carlo dropout U-Net for myocardium segmentation in DCE-MRI.
  • Employed machine learning for landmark localization to divide the myocardium into segments.
  • Calculated MPRI based on left ventricular and myocardial contrast upslopes.
  • Compared automated results with manual adjustments for contours and upslope ranges.

Main Results:

  • Segmental MPRI measurements from the automatic method showed strong correlation with the manual reference (ICC = 0.75).
  • The mean difference between automatic and manual MPRI values was -0.02.
  • The 95% limits of agreement were (-0.86, 0.82), indicating good consistency.

Conclusions:

  • The developed automatic method leverages deep learning segmentation and machine learning landmark detection for MPRI measurement.
  • This approach enables efficient and quantitative assessment of myocardial ischemia in DCE perfusion MRI.
  • The fully automated process eliminates the need for user interaction, enhancing clinical utility.