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Efficient PET-CT image retrieval using graphs embedded into a vector space.

Ashnil Kumar, Jinman Kim, Michael Fulham

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    We developed a faster method for retrieving positron emission tomography-computed tomography (PET-CT) images using vector space embedding. This approach significantly speeds up image retrieval while maintaining high precision for cancer diagnosis and research applications.

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    Area of Science:

    • Medical Imaging
    • Computer Science

    Background:

    • Combined positron emission tomography and computed tomography (PET-CT) enhances cancer diagnosis, localization, and staging by integrating functional and anatomical data.
    • Retrieving PET-CT images from large archives is crucial for diagnosis, education, and research.
    • Current graph-based retrieval methods for PET-CT images are computationally intensive and lack interactive speed.

    Purpose of the Study:

    • To propose a novel, efficient method for retrieving PET-CT images.
    • To overcome the computational complexity and speed limitations of existing graph-based retrieval techniques.

    Main Methods:

    • Developed a vector space embedding method for graph descriptors of PET-CT images.
    • Represented images as graphs and utilized prototype graphs to define a vector space.
    • Measured image dissimilarity based on the distance to fixed-sized prototype graphs.

    Main Results:

    • Achieved a significant speedup of approximately 800× compared to graph-edit distance retrieval (p < 0.05).
    • Maintained comparable retrieval precision with only a 5% difference compared to graph-edit distance (p > 0.05).
    • Demonstrated the feasibility of interactive-rate PET-CT image retrieval.

    Conclusions:

    • The proposed vector space embedding method offers a substantially faster alternative for PET-CT image retrieval.
    • This method preserves the accuracy of retrieval, making it suitable for large-scale medical image archives.
    • Enables efficient use of PET-CT image data for clinical and research purposes.