Literature DB >> 17354686

Maximum entropy spherical deconvolution for diffusion MRI.

Daniel C Alexander1.   

Abstract

This paper proposes a maximum entropy method for spherical deconvolution. Spherical deconvolution arises in various inverse problems. This paper uses the method to reconstruct the distribution of microstructural fibre orientations from diffusion MRI measurements. Analysis shows that the PASMRI algorithm, one of the most accurate diffusion MRI reconstruction algorithms in the literature, is a special case of the maximum entropy spherical deconvolution. Experiments compare the new method to linear spherical deconvolution, used previously in diffusion MRI, and to the PASMRI algorithm. The new method compares favourably both in simulation and on standard brain-scan data.

Mesh:

Year:  2005        PMID: 17354686     DOI: 10.1007/11505730_7

Source DB:  PubMed          Journal:  Inf Process Med Imaging        ISSN: 1011-2499


  39 in total

1.  Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true-tract specific index to characterize white matter diffusion.

Authors:  Flavio Dell'Acqua; Andrew Simmons; Steven C R Williams; Marco Catani
Journal:  Hum Brain Mapp       Date:  2012-04-05       Impact factor: 5.038

2.  Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.

Authors:  Stamatios N Sotiropoulos; Timothy E J Behrens; Saad Jbabdi
Journal:  Neuroimage       Date:  2012-01-14       Impact factor: 6.556

3.  Registration of high angular resolution diffusion MRI images using 4th order tensors.

Authors:  Angelos Barmpoutis; Baba C Vemuri; John R Forder
Journal:  Med Image Comput Comput Assist Interv       Date:  2007

4.  A unified computational framework for deconvolution to reconstruct multiple fibers from diffusion weighted MRI.

Authors:  Bing Jian; Baba C Vemuri
Journal:  IEEE Trans Med Imaging       Date:  2007-11       Impact factor: 10.048

5.  A novel tensor distribution model for the diffusion-weighted MR signal.

Authors:  Bing Jian; Baba C Vemuri; Evren Ozarslan; Paul R Carney; Thomas H Mareci
Journal:  Neuroimage       Date:  2007-05-03       Impact factor: 6.556

6.  Multi-fiber reconstruction from diffusion MRI using mixture of Wisharts and sparse deconvolution.

Authors:  Bing Jian; Baba C Vemuri
Journal:  Inf Process Med Imaging       Date:  2007

7.  Directional functions for orientation distribution estimation.

Authors:  Yogesh Rathi; Oleg Michailovich; Martha E Shenton; Sylvain Bouix
Journal:  Med Image Anal       Date:  2009-02-05       Impact factor: 8.545

8.  Fluid registration of diffusion tensor images using information theory.

Authors:  M C Chiang; A D Leow; A D Klunder; R A Dutton; M Barysheva; S E Rose; K L McMahon; G I de Zubicaray; A W Toga; P M Thompson
Journal:  IEEE Trans Med Imaging       Date:  2008-04       Impact factor: 10.048

9.  Level set fiber bundle segmentation using spherical harmonic coefficients.

Authors:  Mohammad-Reza Nazem-Zadeh; Esmaeil Davoodi-Bojd; Hamid Soltanian-Zadeh
Journal:  Comput Med Imaging Graph       Date:  2009-10-21       Impact factor: 4.790

10.  Optimal diffusion MRI acquisition for fiber orientation density estimation: an analytic approach.

Authors:  Nathan S White; Anders M Dale
Journal:  Hum Brain Mapp       Date:  2009-11       Impact factor: 5.038

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