StatisticallyBased Reorientation of Diffusion Tensor Field - PowerPoint PPT Presentation

About This Presentation
Title:

StatisticallyBased Reorientation of Diffusion Tensor Field

Description:

StatisticallyBased Reorientation of Diffusion Tensor Field – PowerPoint PPT presentation

Number of Views:81
Avg rating:3.0/5.0
Slides: 27
Provided by: xdr3
Learn more at: http://www.columbia.edu
Category:

less

Transcript and Presenter's Notes

Title: StatisticallyBased Reorientation of Diffusion Tensor Field


1
Statistically-Based Reorientation of Diffusion
Tensor Field
July 10, 2002
  • XU, DONGRONG
  • SUSUMU MORI
  • DINGGANG SHEN
  • CHRISTOS DAVATZIKOS

JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE
2
Outline
  • Introduction
  • Motivation
  • Preliminaries
  • Our Method
  • Experiment Results
  • Conclusion
  • Acknowledgement

3
Introduction
  • DTI second order tensor at each voxel
  • A 3 x 3 symmetric matrix
  • The tensor describes local water diffusion
  • DT provides insight into white matter region
    structure

4
Introduction (cont.)
Example 1 3D ellipsoid view
5
Introduction (cont.)
Example 2 Primary direction view
6
Introduction (cont.)
  • Existing DTI warping methods
  • - Small Strain Method
  • - Finite Strain Method
  • Preservation of Principal Direction (PPD)
  • Our Method
  • Reorientation based on Procrustean Estimation

7
Motivation
  • Spatial registration of diffusion tensor images
    (DTI) for statistical analysis, based on noisy
    observations









8
Motivation (cont.)
  • To process DTI in a different space, e.g. track
    neural fibers

9
Preliminaries
Tensor reorientation is a must
Wrong
Correct
Deformed fiber
Deformed Fiber
Original Fiber
10
Preliminaries (cont.)
Scaling component needs to be removed
Wrong
Correct
Deformed fiber
Deformed Fiber
Original Fiber
11
Preliminaries (cont.)
Tensors original orientation is important
Shear Force
12
Preliminaries (cont.)
  • Difficulties
  • Tensor reorientation
  • De-noise estimate the true orientation
  • DTI warping
  • Relocation Reorientation

13
Our Method
  • Reorientation by Procrustean Estimation in an
    optimized neighborhood, based on estimated PDF()

14
Our Method (cont.)
  • Procrustean Estimation
  • Let A,B ?Mmxn ,We need to find a unitary
    matrix U, so that
  • A U . B or minimize (A-U.B)
  • where
  • U V . WT
  • by singular value decomposition (SVD)
  • A . BT V . S . WT

15
Our Method (cont.)
Neighborhood
  • Estimate an optimized neighborhood for
  • True PD
  • PDF resample
  • Keep neighborhood volume a constant

Underlying Fiber
16
Our Method (cont.)
Resample
  • Directly take samples from neighborhood
  • They implicitly follow the local PDF()

17
Our Method (cont.)
Weight Procrustean Estimation
  • Reasons
  • Sample importance varies with distance
  • Tensors fractional anisotropy (FA) factor

18
Experiment 1
Simulated data to demonstrate the
effectiveness of our algorithm
19
Experiment 2
With Real Case Before After Warping
20
Experiment 3
With Simulation Data on 5 Individual Subjects
21
Conclusion
  • Procrustean estimation for tensor reorientation
  • Relatively robust in noisy environment
  • Fiber pathway preserved after warping
  • Preservation of tensor shape (both 1st and 2nd
    PD)
  • No small displacement requirement

22
Acknowledgement
  • Thanks to Mr. Meiyappan Solaiyappan

Thank you ! - END -
23
)
24
Experiment 4
Preserve 1st 2nd PD
25
Experiment 5
1. Improve SNR with 9 real cases



The nine normal subjects
2. Target abnormal areas by FA-map
26
Our Method (cont.)
Write a Comment
User Comments (0)
About PowerShow.com