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

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(Created page with '__NOTOC__ <gallery> Image:PW2009-v3.png|Project Week Main Page Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus ...')
 
 
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__NOTOC__
 
__NOTOC__
 
<gallery>
 
<gallery>
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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]]
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Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
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Image:3DSlicerFourDAnalysis_Screenshot.png|Screen shot of 4D Analysis Module
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Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
 
 
</gallery>
 
</gallery>
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==Instructions for Use of this Template==
 
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#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
 
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#Copy the entire text of this page into the page created above
 
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#Link the created page into the list of projects for the project event
 
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#Delete this section from the created page
 
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#Send an email to tkapur at bwh.harvard.edu if you are stuck
 
  
 
==Key Investigators==
 
==Key Investigators==
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* BWH: Junichi Tokuda, Wendy Plesniak, Nobuhiko Hata
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* Utah: Tom Fletcher, Ross Whitaker
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* WFU:Craig A. Hamilton
  
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
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We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.
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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.
 +
 
  
 
</div>
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.
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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)
  
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Our plan for the project week is to first try out <bar>,...
 
 
</div>
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.
+
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
 +
 
  
 
</div>
 
</div>
 
</div>
 
</div>
  
 +
==References==
  
−
==References==
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The preliminary module implementation is described in [[Slicer3:FourDAnalysis]].
−
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
 
−
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
 
−
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
 
−
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .
 

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.

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