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HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC

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Information Processing in Medical Imaging (IPMI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5636))

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Abstract

We denoise HARDI (High Angular Resolution Diffusion Imaging) data arising in medical imaging. Diffusion imaging is a relatively new and powerful method to measure the 3D profile of water diffusion at each point. This can be used to reconstruct fiber directions and pathways in the living brain, providing detailed maps of fiber integrity and connectivity. HARDI is a powerful new extension of diffusion imaging, which goes beyond the diffusion tensor imaging (DTI) model: mathematically, intensity data is given at every voxel and at any direction on the sphere. However, HARDI data is usually highly contaminated with noise, depending on the b-value which is a tuning parameter pre-selected to collect the data. Larger b-values help to collect more accurate information in terms of measuring diffusivity, but more noise is generated by many factors as well. So large b-values are preferred, if we can satisfactorily reduce the noise without losing the data structure. We propose a variational method to denoise HARDI data by denoising the spherical Apparent Diffusion Coefficient (sADC), a field of radial functions derived from the data. We use vectorial total variation regularization, an L 1 data fidelity term and the logarithmic barrier function in the minimization. We present experiments of denoising synthetic and real HARDI data.

Funded by the NIH Roadmap for Medical Research Grant U54 RR021813 (CCB).

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Author information

Authors and Affiliations

  1. Mathematics Department, UCLA, Los Angeles, CA, USA

    Yunho Kim & Luminita Vese

  2. LONI & Neurology Department, UCLA School of Medicine, Los Angeles, CA, USA

    Paul M. Thompson, Arthur W. Toga & Liang Zhan

Authors
  1. Yunho Kim
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  2. Paul M. Thompson
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  3. Arthur W. Toga
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  4. Luminita Vese
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  5. Liang Zhan
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 North Charles Street, MD 21218, Baltimore, USA

    Jerry L. Prince

  2. Department of Radiology, Johns Hopkins University, 600 North Wolfe Street, MD 21287, Baltimore, USA

    Dzung L. Pham

  3. Division of Imaging and Applied Mathematics, OSEL, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, MD 20993, Silver Spring, USA

    Kyle J. Myers

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© 2009 Springer-Verlag Berlin Heidelberg

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Kim, Y., Thompson, P.M., Toga, A.W., Vese, L., Zhan, L. (2009). HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC . In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_43

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  • DOI: https://doi.org/10.1007/978-3-642-02498-6_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02497-9

  • Online ISBN: 978-3-642-02498-6

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