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Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.

Cian M Scannell1,2, Mitko Veta3, Adriana D M Villa1

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, UK.

Journal of Magnetic Resonance Imaging : JMRI
|November 12, 2019
PubMed
Summary
This summary is machine-generated.

This study demonstrates that a deep learning (DL) pipeline accurately processes cardiac MRI perfusion data, yielding quantitative myocardial blood flow (MBF) values comparable to manual methods. This automated approach simplifies analysis for diagnosing myocardial ischemia.

Keywords:
automated image analysisconvolutional neural networksmachine learningquantitative myocardial perfusion

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Area of Science:

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

Background:

  • Quantitative myocardial perfusion cardiac MRI offers objective assessment of myocardial ischemia.
  • Clinical adoption is limited by complex and time-consuming postprocessing, especially image segmentation.

Purpose of the Study:

  • To assess the effectiveness of an automated deep learning (DL) pipeline for processing cardiac MRI perfusion images.
  • To enable more efficient quantitative analysis of myocardial perfusion.

Main Methods:

  • A retrospective study utilizing 350 MRI scans from 175 patients.
  • A deep learning (DL) pipeline was developed for automated image processing.
  • Performance was evaluated against manual processing using metrics like Dice similarity coefficient and Bland-Altman analysis for myocardial blood flow (MBF).

Main Results:

  • The DL pipeline achieved high accuracy in landmark detection and myocardial segmentation (Dice coefficient 0.80).
  • Automated processing resulted in quantitative MBF values with minimal bias (2.6%) and high agreement (ICC=0.89) compared to manual analysis.
  • Errors in peak signal enhancement detection were low (1.49 timeframes).

Conclusions:

  • The automated DL pipeline demonstrates high accuracy and reliability for processing quantitative myocardial perfusion MRI.
  • DL-based processing provides quantitative results comparable to manual methods, potentially improving clinical workflow efficiency.
  • This technology can facilitate wider clinical use of quantitative myocardial perfusion MRI for diagnosing ischemia.