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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.

Fan Zhang, Kang Ik Kevin Cho, Johanna Seitz-Holland

    IEEE Transactions on Medical Imaging
    |November 9, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Deep Diffusion Parcellation (DDParcel) accurately segments brain regions directly from diffusion MRI data, improving reproducibility and reducing computation time without anatomical MRI. This method enhances tractography analysis for fiber identification.

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    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Accurate brain region parcellation is crucial for diffusion MRI (dMRI) analysis, enabling region-specific quantification and improved tractography.
    • Current parcellation methods often rely on anatomical MRI (T1/T2-weighted) and registration to diffusion space, which is challenging due to dMRI distortions and low resolution, leading to potential mislabeling.
    • These anatomical MRI-dependent methods are not suitable when such data is unavailable.

    Purpose of the Study:

    • To develop a novel deep learning method, Deep Diffusion Parcellation (DDParcel), for fast and accurate brain anatomical region parcellation directly from dMRI data.
    • To overcome the limitations of registration-based methods and provide a viable alternative when anatomical MRI is absent.

    Main Methods:

    • DDParcel utilizes a multi-level fusion deep learning network that takes dMRI parameter maps as input and outputs labels for 101 anatomical regions (FreeSurfer Desikan-Killiany atlas).
    • The network learns the registration of diffusion features to anatomical MRI using Human Connectome Project data.
    • For new subjects, DDParcel predicts parcellation solely from dMRI data, eliminating the need for anatomical MRI.

    Main Results:

    • DDParcel achieved higher test-retest reproducibility and regional homogeneity compared to T1w-based parcellation.
    • The method significantly reduced computational time required for parcellation.
    • Generalizability was confirmed across diverse populations and dMRI acquisition protocols.
    • DDParcel's parcellation improved fiber tract identification in tractography analysis.

    Conclusions:

    • DDParcel offers a fast, accurate, and reproducible method for brain parcellation directly from dMRI data.
    • This deep learning approach overcomes the challenges associated with traditional registration-based methods and expands the applicability of dMRI analysis.
    • DDParcel demonstrates significant utility in enhancing tractography and anatomical specificity in neuroimaging research.