Literature DB >> 19815083

Diffusion orientation transform revisited.

Erick Jorge Canales-Rodríguez1, Ching-Po Lin, Yasser Iturria-Medina, Chun-Hung Yeh, Kuan-Hung Cho, Lester Melie-García.   

Abstract

Diffusion orientation transform (DOT) is a powerful imaging technique that allows the reconstruction of the microgeometry of fibrous tissues based on diffusion MRI data. The three main error sources involving this methodology are the finite sampling of the q-space, the practical truncation of the series of spherical harmonics and the use of a mono-exponential model for the attenuation of the measured signal. In this work, a detailed mathematical description that provides an extension to the DOT methodology is presented. In particular, the limitations implied by the use of measurements with a finite support in q-space are investigated and clarified as well as the impact of the harmonic series truncation. Near- and far-field analytical patterns for the diffusion propagator are examined. The near-field pattern makes available the direct computation of the probability of return to the origin. The far-field pattern allows probing the limitations of the mono-exponential model, which suggests the existence of a limit of validity for DOT. In the regimen from moderate to large displacement lengths the isosurfaces of the diffusion propagator reveal aberrations in form of artifactual peaks. Finally, the major contribution of this work is the derivation of analytical equations that facilitate the accurate reconstruction of some orientational distribution functions (ODFs) and skewness ODFs that are relatively immune to these artifacts. The new formalism was tested using synthetic and real data from a phantom of intersecting capillaries. The results support the hypothesis that the revisited DOT methodology could enhance the estimation of the microgeometry of fiber tissues.

Mesh:

Year:  2009        PMID: 19815083     DOI: 10.1016/j.neuroimage.2009.09.067

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

1.  Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.

Authors:  Pramod Kumar Pisharady; Stamatios N Sotiropoulos; Julio M Duarte-Carvajalino; Guillermo Sapiro; Christophe Lenglet
Journal:  Neuroimage       Date:  2017-06-29       Impact factor: 6.556

2.  A 4D hyperspherical interpretation of q-space.

Authors:  A Pasha Hosseinbor; Moo K Chung; Yu-Chien Wu; Barbara B Bendlin; Andrew L Alexander
Journal:  Med Image Anal       Date:  2015-01-03       Impact factor: 8.545

3.  Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values.

Authors:  Bryce Wilkins; Namgyun Lee; Niharika Gajawelli; Meng Law; Natasha Leporé
Journal:  Neuroimage       Date:  2014-12-30       Impact factor: 6.556

4.  Linear transforms for Fourier data on the sphere: application to high angular resolution diffusion MRI of the brain.

Authors:  Justin P Haldar; Richard M Leahy
Journal:  Neuroimage       Date:  2013-01-24       Impact factor: 6.556

5.  Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.

Authors:  Saad Jbabdi; Stamatios N Sotiropoulos; Alexander M Savio; Manuel Graña; Timothy E J Behrens
Journal:  Magn Reson Med       Date:  2012-02-14       Impact factor: 4.668

6.  Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator.

Authors:  Qiyuan Tian; Grant Yang; Christoph Leuze; Ariel Rokem; Brian L Edlow; Jennifer A McNab
Journal:  Neuroimage       Date:  2019-01-23       Impact factor: 6.556

7.  Histological validation of diffusion MRI fiber orientation distributions and dispersion.

Authors:  Kurt G Schilling; Vaibhav Janve; Yurui Gao; Iwona Stepniewska; Bennett A Landman; Adam W Anderson
Journal:  Neuroimage       Date:  2017-10-23       Impact factor: 6.556

8.  A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

Authors:  Pramod Kumar Pisharady; Stamatios N Sotiropoulos; Guillermo Sapiro; Christophe Lenglet
Journal:  Med Image Comput Comput Assist Interv       Date:  2017-09-04

9.  Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization.

Authors:  Erick J Canales-Rodríguez; Alessandro Daducci; Stamatios N Sotiropoulos; Emmanuel Caruyer; Santiago Aja-Fernández; Joaquim Radua; Jesús M Yurramendi Mendizabal; Yasser Iturria-Medina; Lester Melie-García; Yasser Alemán-Gómez; Jean-Philippe Thiran; Salvador Sarró; Edith Pomarol-Clotet; Raymond Salvador
Journal:  PLoS One       Date:  2015-10-15       Impact factor: 3.240

10.  Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI.

Authors:  Benoit Scherrer; Simon K Warfield
Journal:  PLoS One       Date:  2012-11-26       Impact factor: 3.240

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