Vishal Patel

University of California, Los Angeles, Los Angeles, CA, United States

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Publications (7)12.5 Total impact

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    ABSTRACT: Diffusion tensor imaging (DTI) is sensitive to the directionally- constrained flow of water, which diffuses preferentially along axons. Tractography programs may be used to infer matrices of connectivity (anatomical networks) between pairs of brain regions. Little is known about how these computed connectivity measures depend on the scans' spatial and angular resolutions. To determine this, we scanned 8 young adults with DTI at 2.5 and 3 mm resolutions, and an additional subject at 4 resolutions between 2-4 mm. We computed 70×70 connectivity matrices, using whole-brain tractography to measure fiber density between all pairs of 70 cortical and subcortical regions. Spatial and angular resolution affected the computed connectivity for narrower tracts (internal capsule and cerebellum), but also for the corticospinal tract. Data resolution affected the apparent role of some key structures in cortical anatomic networks. Care is needed when comparing network data across studies, and interpreting apparent disagreements among findings.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
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    Conference Paper: K-SVD for HARDI denoising
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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
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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
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    ABSTRACT: Diffusion-weighted MRI has enabled the imaging of white matter architecture in vivo. Fiber orientations have classically been assumed to lie along the major eigenvector of the diffusion tensor, but this approach has well-characterized shortcomings in voxels containing multiple fiber populations. Recently proposed methods for recovery of fiber orientation via spherical deconvolution utilize a spherical harmonics framework and are susceptible to noise, yielding physically-invalid results even when additional measures are taken to minimize such artifacts. In this work, we reformulate the spherical deconvolution problem onto a discrete spherical mesh. We demonstrate how this formulation enables the estimation of fiber orientation distributions which strictly satisfy the physical constraints of realness, symmetry, and non-negativity. Moreover, we analyze the influence of the flexible regularization parameters included in our formulation for tuning the smoothness of the resultant fiber orientation distribution (FOD). We show that the method is robust and reliable by reconstructing known crossing fiber anatomy in multiple subjects. Finally, we provide a software tool for computing the FOD using our new formulation in hopes of simplifying and encouraging the adoption of spherical deconvolution techniques.
    NeuroImage 03/2010; 51(3):1071-81. · 6.25 Impact Factor
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    ABSTRACT: A wide range of computational methods have been developed for reconstructing white matter geometry from a set of diffusion-weighted images (DWIs), and many clinical studies rely on publicly-available implementations of these methods for analyzing DWI datasets. Unfortunately, the poor interoperability between DWI analysis tools often effectively restricts users to the algorithms provided by a single software suite, which may be suboptimal relative to those in other packages, or outdated given recent developments in the field. A major barrier to data portability and the interoperability between DWI analysis tools is the lack of a standard format for representing and communicating essential DWI-related metadata at various stages of post-processing. In this report, we address this issue by developing a framework for storing metadata in NIfTI for DWI (MiND). We utilize the standard NIfTI format extension mechanism to store essential DWI metadata in an extended header for multiple commonly-encountered DWI data structures. We demonstrate the utility of this approach by implementing a full suite of tools for DWI analysis workflows which communicate solely through the MiND mechanism. We also show that the MiND framework allows for simple, direct DWI data visualization, and we illustrate its effectiveness by constructing a group atlas for 330 subjects using solely MiND-centric tools for DWI processing. Our results indicate that the MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users.
    NeuroImage 03/2010; 51(2):665-76. · 6.25 Impact Factor
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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
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    Vishal Patel, Paul Thompson, Arthur Toga