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    Accelerated Diffusion Operators for Enhancing DW-MRI

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    2010
    Author
    Rodrigues, Paulo
    Duits, Remco
    Romeny, Bart M. ter Haar
    Vilanova, Anna
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    Abstract
    the intra-voxel diffusion pattern compared to its simpler predecessor diffusion tensor imaging (DTI). However, HARDI in general produces very noisy diffusion patterns due to the low SNR from the scanners at high b-values. Furthermore, it still exhibits limitations in areas where the diffusion pattern is asymmetrical (bifurcations, splaying fibers, etc.). To overcome these limitations, enhancement and denoising of the data based on context information is a crucial step. In order to achieve it, convolutions are performed in the coupled spatial and angular domain. Therefore the kernels applied become also HARDI data. However, these approaches have high computational complexity of an already complex HARDI data processing. In this work, we present an accelerated framework for HARDI data regularizaton and enhancement. The convolution operators are optimized by: pre-calculating the kernels, analysing kernels shape and utilizing look-up-tables. We provide an increase of speed, compared to previous brute force approaches of simpler kernels. These methods can be used as a preprocessing for tractography and lead to new ways for investigation of brain white matter.
    BibTeX
    @inproceedings {10.2312:VCBM:VCBM10:049-056,
    booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
    editor = {Dirk Bartz and Charl Botha and Joachim Hornegger and Raghu Machiraju and Alexander Wiebel and Bernhard Preim},
    title = {{Accelerated Diffusion Operators for Enhancing DW-MRI}},
    author = {Rodrigues, Paulo and Duits, Remco and Romeny, Bart M. ter Haar and Vilanova, Anna},
    year = {2010},
    publisher = {The Eurographics Association},
    ISSN = {2070-5786},
    ISBN = {978-3-905674-28-6},
    DOI = {10.2312/VCBM/VCBM10/049-056}
    }
    URI
    http://dx.doi.org/10.2312/VCBM/VCBM10/049-056
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA