Difference between revisions of "2009 Summer Project Week 4D Imaging"

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__NOTOC__
 
__NOTOC__
 
<gallery>
 
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week|Project Week Main Page]]
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Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Project Week Main Page]]
 +
Image:3DSlicerFourDAnalysis_Screenshot.png|Screen shot of 4D Analysis Module
 
</gallery>
 
</gallery>
  
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<h3>Objective</h3>
 
<h3>Objective</h3>
Create a mechanism to handle 4D images in 3D Slicer for perfusion analysis, cardiac, etc.  
+
Implement a set of 3D Slicer modules to handle 4D images in 3D Slicer for perfusion analysis, cardiac, etc.
 +
including:
 +
*Handling
 +
**Loading 4D volume
 +
**Scroll time-line
 +
**4D image editing
 +
**Recording [[2009_Summer_Project_Week_4D_Gated_US_In_Slicer| 4D Gated US]]
 +
*Processing
 +
**Image registration for motion compensation
 +
*Analysis
 +
**Perfusion analysis: fitting pharmacokinetic model
 +
 
 +
*We are going to organize 4D imaging session during the meeting.
 +
Please join us, if you are interested in.
  
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
See [[Slicer3:FourDAnalysis]]
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We will work on the following tasks:
 
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*Implementation of '''4D Image''' module that provides:
 +
**4D image loading: Loading a series of 3D images from a specified director. The data can be either in DICOM or NRRD format.
 +
**Time line scroll-bar interface: scrolling the frame in time-direction. It allows you to scroll the frame for foreground and background screens independently to compare two images at the different time points.
 +
**Frame editing: Reorganizing the time series data (optional)
 +
*Implementation of '''4D Analysis''' module that provides:
 +
**Intensity plot: Plotting temporal changes of intensities at specified regions. This feature is useful for analyzing dynamic contrast images.
 +
**Model fitting: A python interface to analyze intensity curves obtained from the 4D images. The interface is useful to fit pharmacokinetic models to intensity curves to obtain perfusion parameters.
 +
*Investigating BatchMake as an infrastructure for time-series image processing.
 +
**4D Cropping: Cropping volumes in a time-series data using BatchMake.
 +
**4D Image registration: Registering each volume frame to a key-frame to compensate organ motion.
 +
**Image registration using cluster (Optional)
  
 
</div>
 
</div>
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<h3>Progress</h3>
 
<h3>Progress</h3>
 +
Before the project week, we have completed:
 +
*Initial implementation of 4D Image module. The module is available at http://svn.slicer.org/Slicer3/trunk/Modules/FourDImage
 +
*Initial implementation of 4D Analysis module. The Python interface has to be fixed. The module is available at http://svn.slicer.org/Slicer3/trunk/Modules/FourDAnalysis
 +
*[[2009_Summer_Project_Week_4D_Imaging_discussion| 4D Meeting]] was organized.
 +
**Demo of 4D module
 +
**4D Bundle
 +
**Application
 +
**Memory usage
 +
**Pipeline processing
  
  
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==References==
 
==References==
 +
 +
The preliminary module implementation is described in [[Slicer3:FourDAnalysis]].

Latest revision as of 14:40, 26 June 2009

Home < 2009 Summer Project Week 4D Imaging

Key Investigators

  • BWH: Junichi Tokuda, Wendy Plesniak, Nobuhiko Hata
  • WFU:Craig A. Hamilton

Objective

Implement a set of 3D Slicer modules to handle 4D images in 3D Slicer for perfusion analysis, cardiac, etc. including:

  • Handling
    • Loading 4D volume
    • Scroll time-line
    • 4D image editing
    • Recording 4D Gated US
  • Processing
    • Image registration for motion compensation
  • Analysis
    • Perfusion analysis: fitting pharmacokinetic model
  • We are going to organize 4D imaging session during the meeting.

Please join us, if you are interested in.


Approach, Plan

We will work on the following tasks:

  • Implementation of 4D Image module that provides:
    • 4D image loading: Loading a series of 3D images from a specified director. The data can be either in DICOM or NRRD format.
    • Time line scroll-bar interface: scrolling the frame in time-direction. It allows you to scroll the frame for foreground and background screens independently to compare two images at the different time points.
    • Frame editing: Reorganizing the time series data (optional)
  • Implementation of 4D Analysis module that provides:
    • Intensity plot: Plotting temporal changes of intensities at specified regions. This feature is useful for analyzing dynamic contrast images.
    • Model fitting: A python interface to analyze intensity curves obtained from the 4D images. The interface is useful to fit pharmacokinetic models to intensity curves to obtain perfusion parameters.
  • Investigating BatchMake as an infrastructure for time-series image processing.
    • 4D Cropping: Cropping volumes in a time-series data using BatchMake.
    • 4D Image registration: Registering each volume frame to a key-frame to compensate organ motion.
    • Image registration using cluster (Optional)

Progress

Before the project week, we have completed:


References

The preliminary module implementation is described in Slicer3:FourDAnalysis.