V. Patel

Publications (3)0 Total impact

• Source

  Available from: ucla.edu

  Conference Proceeding: K-SVD for HARDI denoising

  V. Patel, Yonggang Shi, P.M. Thompson, A.W. Toga
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  ABSTRACT: Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.
  Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on; 05/2011
• Source

  Available from: Katie Mcmahon

  Conference Proceeding: Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

  V. Patel, Ming-Chang Chiang, P.M. Thompson, K.L. McMahon, G.I. de Zubicaray, N.G. Martin, M.J. Wright, A.W. Toga
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  ABSTRACT: Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.
  Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 05/2010
• Source

  Available from: psu.edu

  Conference Proceeding: Mesh-based spherical deconvolution for physically valid fiber orientation reconstruction from diffusion-weighted MRI

  V. Patel, Yonggang Shi, P. Thompson, A. Toga
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  ABSTRACT: High angular resolution diffusion imaging (HARDI) methods have enabled the reconstruction of complex spin diffusion profiles in central nervous system white matter through diffusion-weighted MRI. For recovery of the underlying fiber orientations, conventional spherical deconvolution techniques based on spherical harmonics typically have difficulty producing fiber orientation distributions (FODs) that simultaneously satisfy the physical constraints of being real, symmetric, and non-negative. In this work, we propose a novel approach for HARDI reconstruction that is guaranteed to generate FODs satisfying these constraints. By using a meshed representation of the unit sphere, we formulate the spherical deconvolution as a convex optimization problem and compute the solution using a projected gradient descent algorithm. Flexible regularization is also included in our method to allow for tuning the sharpness of the reconstructed FOD. In our experiments, we present simulated results to examine the effects of varying the regularization parameters, and we illustrate the robustness of our method by applying it to several biological data sets to reconstruct known white matter fiber geometry.
  Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on; 08/2009