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Bayesian Aneurysm Growth Detection via Surface Displacement Modeling.

Jorge A Roa Castro, Abhishek Singh, Atharva Hans

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    This summary is machine-generated.

    Detecting intracranial aneurysm growth is crucial for clinical decisions. A new Bayesian model using vessel displacement improves growth detection accuracy and reduces reliance on manual measurements from magnetic resonance angiography (MRA).

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    Area of Science:

    • Medical Imaging
    • Radiology
    • Computational Biology

    Background:

    • Clinical management of unruptured intracranial aneurysms relies on detecting growth via serial magnetic resonance angiography (MRA).
    • Current methods using manual 2D diameters or volumetric changes are subjective, prone to variability, and may miss subtle growth.
    • Distinguishing true aneurysm growth from imaging or processing artifacts remains a challenge.

    Purpose of the Study:

    • To develop and validate a novel Bayesian displacement-based model for accurate and reliable assessment of intracranial aneurysm growth from longitudinal MRA.
    • To improve the objectivity and reproducibility of aneurysm growth detection compared to existing methods.
    • To provide calibrated probabilistic outputs to aid clinical decision-making.

    Main Methods:

    • A Bayesian displacement model was developed, utilizing the surrounding vasculature as an internal reference to correct for imaging and processing variability.
    • Longitudinal MRA surface data were registered, and normal-directed displacements were computed.
    • Aneurysm growth was quantified by comparing mean aneurysm displacement to mean displacement of the reference vessel segment.
    • The model was evaluated on two independent MRA cohorts with varying imaging sequences and rater expertise.

    Main Results:

    • The Bayesian displacement model demonstrated strong discrimination of aneurysm growth, achieving an Area Under the Curve (AUC) of 0.86-0.87.
    • The model significantly improved agreement with expert labels (Cohen's kappa up to 0.66) compared to volumetric criteria (kappa 0.35).
    • Performance remained robust across different MRA sequences and was preserved when trained on labels from less experienced raters.

    Conclusions:

    • The proposed Bayesian displacement model offers an interpretable, probabilistic framework for assessing intracranial aneurysm growth from longitudinal MRA.
    • This method reduces dependence on subjective manual measurements and clinician expertise, enhancing diagnostic accuracy and consistency.
    • Calibrated uncertainty estimates can guide clinical decisions, potentially optimizing surveillance strategies and reducing unnecessary interventions.