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Automated Aortic Anatomy Analysis: from Image to Clinical Indicators.

M Lahlouh, Y Chenoune, R Blanc

    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
    Summary
    This summary is machine-generated.

    This study introduces a new framework to analyze aortic arch anatomy for endovascular procedures. It helps clinicians predict navigational challenges, improving catheter selection and reducing complications in cerebrovascular disease treatment.

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    Area of Science:

    • Medical Imaging
    • Interventional Neuroradiology
    • Computational Anatomy

    Background:

    • Cerebrovascular diseases are often treated using endovascular catheter navigation from the groin to the brain.
    • Complex aortic arch and supra-aortic vessel anatomy pose significant challenges for catheter selection and navigation.
    • These anatomical complexities can lead to prolonged procedures, increased complications, and treatment failures.

    Purpose of the Study:

    • To develop and validate a computational framework for analyzing the anatomy of the aortic arch and supra-aortic trunks.
    • To automatically extract key anatomical and geometrical features relevant to endovascular navigation.
    • To enhance pre-procedural planning for interventional neuroradiology.

    Main Methods:

    • A framework was developed to analyze patient-specific meshes of the aortic arch and supra-aortic vessels.
    • A Convolutional Neural Network (CNN)-based pipeline was used for prior mesh segmentation.
    • The framework automatically computes features like arch type, tortuosity, and angulations.
    • Quantitative and qualitative validation was performed by experienced neuroradiologists.

    Main Results:

    • The framework reliably characterizes the anatomy of the aortic arch and supra-aortic trunks.
    • Computed features accurately describe navigational difficulties encountered during catheterization.
    • Validation confirmed the reliability and clinical relevance of the vessel characterization.

    Conclusions:

    • This automated framework provides clinicians with crucial pre-procedural anatomical insights.
    • It facilitates optimal catheter selection and navigation planning for endovascular procedures in the aortic arch region.
    • The method has the potential to improve outcomes and reduce complications in treating cerebrovascular diseases.