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Simulation and Synthesis in Medical Imaging.

Alejandro F Frangi, Sotirios A Tsaftaris, Jerry L Prince

    IEEE Transactions on Medical Imaging
    |September 26, 2018
    PubMed
    Summary

    This editorial clarifies simulation and synthesis in medical imaging, highlighting the synergy between mechanistic and data-driven models for generating high-quality medical images across various applications.

    Area of Science:

    • Medical Imaging
    • Computational Modeling
    • Artificial Intelligence in Healthcare

    Background:

    • This editorial introduces a Special Issue focused on simulation and synthesis in medical imaging.
    • It addresses the need to define ambiguous terms like 'simulation' and 'synthesis' within this field.
    • The importance of both hypothesis-driven (mechanistic) and data-driven (phenomenological) models for medical image generation is discussed.

    Discussion:

    • The Special Issue features twelve papers, with 5 focusing on mechanistic and 7 on phenomenological approaches to medical image generation.
    • Applications span diverse medical fields including cardiology, retinopathy, histopathology, neurosciences, and oncology.
    • The papers cover a wide range of mainstream diagnostic medical imaging modalities.

    Key Insights:

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    • Clarifies the distinct yet synergistic roles of simulation and synthesis in advancing medical imaging.
    • Emphasizes the complementary strengths of mechanistic and phenomenological modeling for image generation.
    • Highlights the broad applicability and impact of these techniques across multiple medical specialties and imaging types.

    Outlook:

    • Identifies current challenges and future research opportunities in medical image simulation and synthesis.
    • Suggests potential for improved diagnostic accuracy and personalized medicine through advanced imaging techniques.
    • Encourages further interdisciplinary collaboration to push the boundaries of medical imaging innovation.