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Updated: Jul 8, 2025

MRI and PET in Mouse Models of Myocardial Infarction
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3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks.

Marcel Beetz, Yilong Yang, Abhirup Banerjee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
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    This study introduces a novel 3D cardiac shape analysis using point clouds for improved myocardial infarction (MI) detection. The method enhances prediction accuracy by analyzing complete heart geometry, outperforming current single-value biomarkers.

    Area of Science:

    • Cardiovascular Imaging
    • Medical Artificial Intelligence
    • Geometric Deep Learning

    Background:

    • Current myocardial infarction (MI) diagnosis relies on single imaging biomarkers, which oversimplify complex 3D cardiac structures and physiology.
    • This limitation hinders accurate understanding and prediction of MI outcomes.
    • There is a need for advanced analytical methods that capture the full cardiac geometry.

    Purpose of the Study:

    • To investigate the utility of complete 3D cardiac shapes, represented as point clouds, for improved MI detection and prediction.
    • To develop and validate a fully automatic pipeline for 3D cardiac shape analysis in MI assessment.
    • To enhance diagnostic accuracy beyond traditional single-valued metrics.

    Main Methods:

    • A multi-step pipeline involving 3D cardiac surface reconstruction and a point cloud classification network was developed.

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    Related Experiment Videos

    Last Updated: Jul 8, 2025

    MRI and PET in Mouse Models of Myocardial Infarction
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    Published on: December 19, 2013

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    3D Whole-heart Myocardial Tissue Analysis
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  • Geometric deep learning techniques were applied for efficient multi-scale learning on high-resolution cardiac surface models.
  • The approach was evaluated on 1068 UK Biobank subjects for prevalent and incident MI detection/prediction.

    • Main Results:

    • The 3D shape analysis pipeline demonstrated significant improvements: approximately 13% for prevalent MI detection and 5% for incident MI prediction over clinical benchmarks.
    • Analysis revealed the contribution of individual ventricles and cardiac phases to MI detection accuracy.
    • Visual analysis identified specific morphological and physiological patterns associated with MI outcomes.

    Conclusions:

    • Complete 3D cardiac shape analysis using point clouds offers a more comprehensive approach to MI detection and prediction.
    • The developed automatic pipeline can serve as a real-time diagnostic tool, uncovering intricate biomarkers for improved clinical decision-making.
    • This method has the potential to significantly enhance the predictive accuracy of myocardial infarction.