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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
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Multimodal structural MRI synthesis pipeline across age.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    This study developed a deep learning model to synthesize T1-weighted and T2-weighted magnetic resonance imaging (MRI) data across the lifespan. The model generates high-quality synthetic MRI, enabling reliable age-related brain changes analysis.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence
    • Medical Image Analysis

    Background:

    • T1-weighted (T1w) and T2-weighted (T2w) MRI are crucial for brain morphology but are resource-intensive.
    • Acquiring both MRI types is challenging for populations with motion issues, like children and the elderly.
    • Deep learning offers potential for synthesizing missing MRI contrasts.

    Purpose of the Study:

    • To develop a deep learning-based cross-modal synthesis model for T1w and T2w MRI.
    • To incorporate age information across the entire lifespan into the synthesis model.
    • To evaluate the model's performance and its ability to capture age-related brain changes.

    Main Methods:

    • Proposed a deep learning cross-modal synthesis model for T1w and T2w MRI.
    • Trained and validated the model on diverse age cohorts (early development, young adulthood, elderly).
    • Assessed image quality using mean squared error and peak signal-to-noise ratio; analyzed age-related effects on brain metrics.

    Main Results:

    • Achieved high similarity between actual and synthesized MRI (low MSE, high PSNR across cohorts).
    • Demonstrated reliable detection of age-related effects in synthesized data.
    • Observed expected patterns like decreased microstructure profile covariance gradient and cortical thinning in specific brain regions.

    Conclusions:

    • The developed pipeline effectively synthesizes multimodal MRI data.
    • This approach is valuable for participants experiencing difficulties during MRI acquisition.
    • The model facilitates the study of age-related brain development and aging using synthetic MRI data.