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Diffeomorphic brain mapping based on T1-weighted images: Improvement of registration accuracy by multichannel mapping

Authors

  • Aigerim Djamanakova BS,

    1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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  • Andreia V. Faria MD, PhD,

    1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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  • John Hsu BS,

    1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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  • Can Ceritoglu PhD,

    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA
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  • Kenichi Oishi MD, PhD,

    1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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  • Michael I. Miller PhD,

    1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
    2. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA
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  • Argye E. Hillis MD,

    1. Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
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  • Susumu Mori PhD

    Corresponding author
    1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
    2. F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
    • The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 330 Traylor Building, 720 Rutland Ave., Baltimore, MD 21205
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Abstract

Purpose:

To improve image registration accuracy in neurodegenerative populations.

Materials and Methods:

This study used primary progressive aphasia, aged control, and young control T1-weighted images. Mapping to a template image was performed using single-channel Large Deformation Diffeomorphic Metric Mapping (LDDMM), a dual-channel method with ventricular anatomy in the second channel, and a dual-channel with appendage method, which utilized a priori knowledge of template ventricular anatomy in the deformable atlas.

Results:

Our results indicated substantial improvement in the registration accuracy over single-contrast-based brain mapping, mainly in the lateral ventricles and regions surrounding them. Dual-channel mapping significantly (P < 0.001) reduced the number of misclassified lateral ventricle voxels (based on a manually defined reference) over single-channel mapping. The dual-channel (w/appendage) method further reduced (P < 0.001) misclassification over the dual-channel method, indicating that the appendage provides more accurate anatomical correspondence for deformation.

Conclusion:

Brain anatomical mapping by shape normalization is widely used for quantitative anatomical analysis. However, in many geriatric and neurodegenerative disorders, severe tissue atrophy poses a unique challenge for accurate mapping of voxels, especially around the lateral ventricles. In this study we demonstrate our ability to improve mapping accuracy by incorporating ventricular anatomy in LDDMM and by utilizing a priori knowledge of ventricular anatomy in the deformable atlas. J. Magn. Reson. Imaging 2013;37:76–84. © 2012 Wiley Periodicals, Inc.

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