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    This study introduces a novel method to generate synthetic Computed Tomography (sCT) images from Electrical Impedance Tomography (EIT) data. This radiation-free approach enhances medical imaging accessibility and diagnostic capabilities, particularly for pulmonary applications.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Computed Tomography (CT) is vital for pulmonary imaging but faces challenges with radiation, cost, and accessibility.
    • Electrical Impedance Tomography (EIT) offers a radiation-free, portable alternative, suitable for bedside and resource-limited environments.

    Purpose of the Study:

    • To demonstrate the feasibility of generating high-resolution synthetic CT (sCT) images from raw EIT voltage data.
    • To enhance EIT's clinical utility by combining its benefits with CT imaging.

    Main Methods:

    • A diffusion transformer model conditioned on EIT voltage measurements was developed for EIT-to-CT image translation.
    • The model was trained on synthetic data and validated using simulated and experimental EIT data.

    Main Results:

    • Generated sCT images were anatomically coherent and visually realistic, achieving high scores in pixel-level and distribution-based metrics (e.g., CC: 0.9282, FID: 33.36).
    • Clinical relevance was confirmed by comparable lung cancer classification performance and expert validation (>90% anatomically correct/realistic).

    Conclusions:

    • The proposed EIT-to-CT translation method effectively generates high-quality sCT images, bridging the gap between EIT and CT imaging.
    • This radiation-free approach holds significant potential for improving medical imaging accessibility and diagnostic accuracy in various clinical settings.