Estimation of fiber orientation and spin density distribution by diffusion deconvolution

نویسندگان

  • Fang-Cheng Yeh
  • Van J. Wedeen
  • Wen-Yih Isaac Tseng
چکیده

A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes. A phantom study was conducted to compare the angular resolution of the fODF with the dODF, and the in vivo datasets were acquired using single-shell, two-shell, and grid sampling schemes, which were then reconstructed by QBI, GQI, and DSI, respectively. The phantom study showed that the fODF significantly improved the angular resolution over the dODF at 45- and 60-degree crossing angles. The in vivo study showed consistent fODF regardless of the applied sampling schemes and reconstruction methods, and the ability to resolve crossing fibers was improved in reduced sampling condition. The fiber spin density obtained from deconvolution showed a higher contrast-to-noise ratio than the fractional anisotropy (FA) mapping, and further application on tractography showed that the fiber spin density can be used to determine the termination of fiber tracts. In conclusion, the proposed deconvolution method is generally applicable to different q-space imaging methods. The calculated fODF improves the angular resolution and also provides a quantitative index of fiber spin density to refine fiber tracking.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Determination of Fiber Direction in High Angular Resolution Diffusion Images using Spherical Harmonics Functions and Wiener Filter

Diffusion tensor imaging (DTI) MRI is a noninvasive imaging method of the cerebral tissues whose fibers directions are not evaluated correctly in the regions of the crossing fibers. For the same reason the high angular resolution diffusion images (HARDI) are used for estimation of the fiber direction in each voxel. One of the main methods to specify the direction of fibers is usage of the spher...

متن کامل

Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images

Diffusion MR imaging provides a unique tool to probe the microgeometry of nervous tissue and to explore the wiring diagram of the neural connections noninvasively. Generally, a forward model is established to map the intra-voxel fiber architecture onto the observable diffusion signals, which is reformulated in this article by adopting a measure-theoretic approach. However, the inverse problem, ...

متن کامل

Mesh-based spherical deconvolution: A flexible approach to reconstruction of non-negative fiber orientation distributions

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 sp...

متن کامل

Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI

Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR-SD),...

متن کامل

Diffusion Gradient Calibration Influences the Accuracy of Fibre Orientation Density Function Estimation: Validation by Efficiency Measure

Introduction. Diffusion-weighted (DW) MRI provides important information regarding the arrangement of white matter fibres. However, imperfections in the DW gradients may cause errors in the estimation of diffusion parameters. The sources of the gradient errors are various and may arise from long-term eddy currents, background gradients, imaging gradients, and spatial non-linearity and non-unifo...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • NeuroImage

دوره 55 3  شماره 

صفحات  -

تاریخ انتشار 2011