sharpening improves clinically feasible q ball imaging reconstructions
Download
Skip this Video
Download Presentation
Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions

Loading in 2 Seconds...

play fullscreen
1 / 17

Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions - PowerPoint PPT Presentation


  • 58 Views
  • Uploaded on

Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions. Maxime Descoteaux & Rachid Deriche Project Team Odyssee INRIA Sophia Antipolis, France. dODF. dODF min-max. dODF min-max. fODF. Improving angular resolution of Q-Ball Imaging.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions' - samson


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
sharpening improves clinically feasible q ball imaging reconstructions

Sharpening Improves Clinically Feasible Q-Ball Imaging Reconstructions

Maxime Descoteaux & Rachid Deriche

Project Team Odyssee

INRIA Sophia Antipolis, France

improving angular resolution of q ball imaging

dODF

dODF min-max

dODF min-max

fODF

Improving angular resolution of Q-Ball Imaging
  • Can we transform the diffusion ODF (dODF) into a sharp fiber ODF (fODF)?
in the literature

=

Fiber response

function

HARDI Signal

FOD

In the literature…
  • Fiber orientation density (FOD) function
  • Spherical Deconvolution

[Tournier et al 2004-2005-2006-2007, Alexander et al 2005, Anderson 2005, Dell’Acqua et al 2007]

sketch of the method
Sketch of the method

=

Convolution assumption

sketch of the method1

FRT

HARDI Signal

dODF

fODF

Deconvolution

sharpening

Sketch of the method
  • A deconvolution approach
step 1 analytical odf estimation

Laplace-Beltrami regularized estimation of the HARDI signal

[Descoteaux et al MRM 2006 & MRM 2007 accepted]

Step 1: Analytical ODF estimation

[Anderson MRM 05, Hess et al MRM 06, Descoteaux et al RR 05, ISBI 06]

step 2 diffusion odf kernel for deconvolution

[Tuch MRM 2004

Descoteaux RR 2005]

Analytical ODF

where e1 > e2 are e-values of D and t := cos

Step 2: Diffusion ODF kernel for deconvolution
  • Estimate from real data
  • Take 300 voxels with highest FA
    • Assumed to contain a single fiber population
  • Find average prolate tensor D that fits the data
  • Diffusion ODF kernel is
step 3 deconvolution with the funk hecke theorem
Step 3: Deconvolution with the Funk-Hecke theorem
  • Final sharp fiber ODF
  • Linear transformation of the spherical harmonic coefficients describing the signal

[Descoteaux et al Research Report 2005, MRM 2007 accepted.]

summary of the method

HARDI Signal

dODF

fODF

Deconvolution

Sharpening

Summary of the method

Analytical

FRT

cj

fj

2 Plj(0)

separation angle
Separation angle

Sharp fiber ODF

Min-max normalized ODF

(Two-tensor model, FA1 = FA2 = 0.7, SNR 30, b-value 3000 s/mm2, 60 DWI)

simulation results

~20

improvement

Mean angular error 4.5 +- 1.23

Simulation results
  • Sharpening improves angular resolution and improves fiber detection with small angular error on the detected maxima
real data acquisition
Real data acquisition
  • N = 60 directions
  • 72 slices, 128 x 128
  • 1.7 mm3 voxels
  • b-value 1000 s/mm2
  • Sharp fiber ODF estimation of order 4 in less than 20 seconds

[Thanks to Max Planck Institute, Leipzig, Germany]

crossing voxel between motor stripe and slf
Crossing voxel between motor stripe and SLF

Unequal volume fraction of the 2 fiber compartments

Voxel manually chosen by expert.

take home message
Take home message
  • It is possible to transform the diffusion ODF into a sharp fiber ODF for clinical QBI acquisitions
  • Method is:
    • Linear, fast, analytic, robust to noise
  • All this possible because of the properties of the spherical harmonics and the Funk-Heck theorem
current work and perspectives
Current work and perspectives…
  • Compare with spherical deconvolution
    • Study the link between the two approaches
    • Study the negative lobe problem that appears with spherical deconvolution [see Tournier et al 2007, Sakaie et al 2007 and Dell’Acqua et al 2007]
  • Use the fiber ODF for tracking
    • Deterministic
    • Probabilistic
thank you

Thank You!

Key References:

  • Descoteaux et al, Regularized, Fast and Robust Analytical Q-Ball Imaging, MRM 2007
  • Descoteaux et al, ISBI 2006 & INRIA Research Report 2005
  • D. Tuch, Q-Ball Imaging, MRM 2004
  • Tournier et al, … Spherical Deconvolution…, NeuroImage 2004 & 2007
  • http://www-sop.inria.fr/odyssee

Thanks to:

-A. Anwander & T. Knosche of the Max Planck Institute, Leipzig, Germany

-C. Poupon et al, Neurospin, Saclay, Paris

ad