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

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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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See [[Slicer3:FourDAnalysis]]
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We will work on the following tasks:
−
*'''Loading 4D image.''' We will implement a feature that allows the user to load a series of 3D images from a specified director. The data can be either in DICOM or NRRD format.
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*Implementation of '''4D Image''' module that provides:
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*'''Time line.''' We will develop a scroll-bar interface to scroll 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.
+
**4D image loading: Loading a series of 3D images from a specified director. The data can be either in DICOM or NRRD format.
−
*'''Motion compensation.''' We will develop a method to compensate organ motion (respiratory motion, cardiac motion etc.)
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**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.
−
*'''Intensity plot.''' We will implement an interface to plot temporal changes of intensities at specified regions. This feature is useful for analyzing dynamic contrast images.
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**Frame editing: Reorganizing the time series data (optional)
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*'''Model fitting.''' The module provides a python interface to analyze intensity curves obtained from the 4D images. The interface will be used to fit pharmacokinetic models to intensity curves to obtain perfusion parameters.
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*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.
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**4D Cropping: Cropping volumes in a time-series data using BatchMake.
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**4D Image registration: Registering each volume frame to a key-frame to compensate organ motion.
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**Image registration using cluster (Optional)
  
 
</div>
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
 +
Before the project week, we have completed:
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*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/FourDImage
  
 +
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The preliminary module implementation is described in [[Slicer3:FourDAnalysis]].
  
 
</div>
 
</div>

Revision as of 15:49, 20 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:

  • Manipulation
    • Loading 4D volume
    • Scroll time-line
    • Edit frames
  • Processing
    • Image registration for motion compensation
  • Analysis
    • Perfusion analysis: fitting pharmacokinetic model

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:


The preliminary module implementation is described in Slicer3:FourDAnalysis.

References

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