Difference between revisions of "Projects/Diffusion/2007 Project Week DTI Registration"

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<h1>Objective</h1>
 
<h1>Objective</h1>
 
We want to implement the Mutual Information metric using partial voluming  for  the registration of multi-modal images.
 
We want to implement the Mutual Information metric using partial voluming  for  the registration of multi-modal images.
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<h1>Progress</h1>
 
<h1>Progress</h1>
* 3D mutual information metric has been implemented
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* finished 3D mutual information metric coding
* Register T2 diffusion weighted image to its own baseline T2 image  
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* tested to register T2 diffusion weighted image to its own baseline T2 image
 
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* with the help of Luis, rewrote the code into ITK filter
 
 
 
 
 
 
  
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Future Work
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* test the algorithm using Autism data and data from MIND
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* compare the acuracy between this MI metric and Matte Metric 
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=Additional Information=
 
 
==Additional Information==
 
 
[[Image:ptv.jpg|thumb|left|480px|Plot of MI versus xyz translation using PTV (T1 and T2 images)]]
 
[[Image:ptv.jpg|thumb|left|480px|Plot of MI versus xyz translation using PTV (T1 and T2 images)]]
 
[[Image:mattes.jpg|thumb|left|480px|Same plot using Mattes ]]
 
[[Image:mattes.jpg|thumb|left|480px|Same plot using Mattes ]]

Latest revision as of 13:40, 29 June 2007

Home < Projects < Diffusion < 2007 Project Week DTI Registration















Key Investigators

  • Utah: Ran Tao, Tom Fletcher, Ross Whitaker

Objective

We want to implement the Mutual Information metric using partial voluming for the registration of multi-modal images. The aim is to correct EPI distortions in diffusion-weighted images (DWIs).

Approach, Plan

Our approach is to register the DWIs to the baseline (B0) image using affine transformations and mutual information. We are implementing a version of MI that interpolates both joint histogram and partial voluming. This is smoother than the Mattes MI currently in ITK.

Progress

  • finished 3D mutual information metric coding
  • tested to register T2 diffusion weighted image to its own baseline T2 image
  • with the help of Luis, rewrote the code into ITK filter

Future Work

  • test the algorithm using Autism data and data from MIND
  • compare the acuracy between this MI metric and Matte Metric


Additional Information

Plot of MI versus xyz translation using PTV (T1 and T2 images)
Same plot using Mattes





Reference

  • Wei.M, Liu.Jundong and Liu.Junhong Artifact reduction in mutual-information-based CT-MR image registration in Medical Imaging 2004: Image Processing
  • Liu.J Artifacts reduction in mutual information-based image registration using prior information in Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference