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Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data

Dilek M Yalcinkaya1,2, Khalid Youssef1,3, Bobak Heydari4

  • 1Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine, Indianapolis, IN, USA.

Arxiv
|August 16, 2024
PubMed
Summary

A novel deep learning method, Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, significantly improves the accuracy of myocardial perfusion MRI segmentation across different centers and scanners. This approach enhances the reliability of automated analysis for ischemic heart disease diagnosis.

Keywords:
artificial intelligencedeep learningdeep neural networksfirst-pass perfusionimage analysisimage segmentationischemic heart diseasemulti-vendormyocardial perfusion MRIpatient adaptivestress perfusion

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Area of Science:

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

Background:

  • Fully automatic analysis of myocardial perfusion MRI is crucial for objective reporting of stress/rest studies in suspected ischemic heart disease.
  • Developing robust deep learning (DL) models for multi-center MRI datasets presents challenges due to variations in software and hardware.
  • Existing DL techniques struggle with data heterogeneity from different acquisition protocols and scanner vendors.

Purpose of the Study:

  • To develop and evaluate a novel DL approach for robust segmentation of myocardial perfusion MRI datasets.
  • To enhance the accuracy and reliability of automated analysis across multi-center, multi-vendor, and multi-protocol MRI data.
  • To address the limitations of current DL methods in handling variations in pulse sequences and scanner hardware.

Main Methods:

  • A Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis approach was developed using a pool of deep neural networks (DNNs) with a U-Net architecture.
  • A space-time sliding-patch analysis generated pixel-wise uncertainty maps, which were used to select the best segmentation outcome from the DNN pool.
  • The DAUGS approach was trained and validated on an internal dataset and tested on two external datasets with variations in pulse sequence and scanner vendor.

Main Results:

  • DAUGS analysis demonstrated comparable performance to established methods on the internal dataset (Dice score: 0.896 ± 0.050).
  • DAUGS significantly outperformed the established approach on external datasets (exD-1 Dice: 0.885 ± 0.040; exD-2 Dice: 0.811 ± 0.070).
  • The proposed method substantially reduced segmentation failures compared to the established approach (4.3% vs. 17.1%).

Conclusions:

  • The DAUGS analysis approach shows significant potential for improving the robustness of DL-based segmentation for multi-center myocardial perfusion MRI.
  • This method effectively handles variations in pulse sequences, site locations, and scanner vendors, leading to more reliable automated analysis.
  • DAUGS offers a promising solution for consistent and accurate myocardial segmentation in clinical settings with diverse imaging data.