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

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Journal of Magnetic Resonance Imaging (Impact Factor: 3.21). 01/2013; 37(1). DOI: 10.1002/jmri.23790
Source: PubMed


To improve image registration accuracy in neurodegenerative populations.
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.
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.
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.

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    • "accuracy of the image transformation and subsequent atlas warping. Our past publications reported a high level of accuracy using the LDDMM algorithm for populations with marked atrophy (Oishi, 2009; Djamanakova, 2013), although it depends on the structure in question. "
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    • "While most of the “atlas-based” approaches previously have targeted the gray matter areas of single contrast images, the atlas used in our approach is multimodal, which means that the atlas consists of a set of images with different contrasts [e.g., T1- and T2-weighted images, Diffusion Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI), and Susceptibility Weighted Image (SWI) contrasts] to allow multimodal image analysis of both gray and white matter structures in the common anatomical framework. The multimodal capability is supported by the Large Deformation Diffeomorphic Metric Mapping (LDDMM) methods that employ single and multi-channel algorithms (Beg et al., 2005; Ceritoglu, 2008; Ceritoglu et al., 2009; Djamanakova et al., 2013), allowing for the incorporation of multiple imaging modalities while performing simultaneous mapping that maximally satisfies registration of the multiple modalities. "
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