Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology

Authors

  • Ukwatta Eranga,

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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    • a)

      Author to whom correspondence should be addressed. Electronic mail: eukwatt1@jhu.edu

  • Arevalo Hermenegild,

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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  • Rajchl Martin,

    1. Department of Computing, Imperial College London, London SW7 2AZ, United Kingdom
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  • White James,

    1. Stephenson Cardiovascular MR Centre, University of Calgary, Calgary, Alberta T2N 2T9, Canada
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  • Pashakhanloo Farhad,

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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  • Prakosa Adityo,

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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  • Herzka Daniel A.,

    1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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  • McVeigh Elliot,

    1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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  • Lardo Albert C.,

    1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 and Division of Cardiology, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21224
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  • Trayanova Natalia A.,

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205; and Department of Biomedical Engineering, Johns Hopkins Institute of Medicine, Baltimore, Maryland 21205
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    • b)

      N. A. Trayanova and F. Vadakkumpadan contributed equally to this work.

  • Vadakkumpadan Fijoy

    1. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland 21205 and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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    • b)

      N. A. Trayanova and F. Vadakkumpadan contributed equally to this work.


Abstract

Purpose:

Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need.

Methods:

The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method.

Results:

The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs.

Conclusions:

The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

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