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Technical Aspect of the Automated Synthesis and Real-Time Kinetic Evaluation of [11C]SNAP-7941
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Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

William J Allen, Refaat E Gabr, Getaneh B Tefera

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    |March 6, 2018
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    This study presents a platform for real-time analysis of magnetic resonance imaging (MRI) data using software automation and high-performance computing (HPC). This enables faster, automated MRI analysis for improved clinical decision support.

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    Area of Science:

    • Biomedical data science
    • Medical imaging analysis
    • High-performance computing applications

    Background:

    • Biomedical data volume and variety are increasing, offering potential for enhanced clinical decision support.
    • Current clinical data analysis, particularly for medical imaging like MRI, can be time-consuming and limit real-time applications.

    Purpose of the Study:

    • To demonstrate a robust platform for real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data.
    • To leverage software automation and high-performance computing (HPC) resources for immediate data processing.

    Main Methods:

    • Utilized the Agave application programming interface (API) for seamless communication, data transfer, and job control.
    • Integrated Agave with an MRI scanner and an off-site HPC resource.
    • Employed the GRAphical Pipeline Environment (GRAPE) tool via Agave for automated, real-time, quantitative MRI analysis.

    Main Results:

    • Successfully implemented a platform for automated, real-time, quantitative analysis of MRI scans.
    • Demonstrated the feasibility of same-session image processing for MRI data.
    • Established a foundation for adaptive scanning and real-time quality control in medical imaging.

    Conclusions:

    • The developed platform enables real-time analytics of MRI data, enhancing clinical decision support.
    • Same-session image processing has the potential to accelerate pathology discovery and reduce patient callbacks.
    • The platform architecture is adaptable for other medical instruments, HPC resources, and analytical tools.