Difference between revisions of "2009 Summer project week adaptive radiation planning visualization"

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<h3>Objective</h3>
 
<h3>Objective</h3>
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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We are applying functional magnetic resonance imaging, including BOLD and DTI to study the feasibility of adaptive radiation therapy treatment planning and longitudinal tracking/visualization. This is our initial effort and we are concentrating on the application of white matter tractography to improved clinical target volume delineation, tumor characterization and theraputic effects tracking.
 
 
  
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference belowThe main challenge to this approach is <foo>.
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Our approach will be to add DTI pulse sequences to the end of the standard brain MT protocol on our GE HDx 3T magnet and then, along with the routine tissue density mapping performed in RadOnc using CT, perform parallel dosimetry planning procedures - one using just the standard brain MR protocol and one fusing the added white matter tractography FA maps/reconstructions with the MR and CT imagesWe expect to observe improved delineation of tumor margins, improvements in treatment responses as measured by standard clinical measures and potential prognostic value for patient signs/symptoms course based on the identified white matter tracks involved.<foo>.
 
 
Our plan for the project week is to first try out <bar>,...
 
  
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Our plan for the project week is to find out what, if any, DICOM-RT import/export functions are available in slicer and design a composite visualization method able to overlay the results from sequential followup scans as a longitudinal tracking tool. 
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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.
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Scanning is ready to begin and we are ready to begin accumulating cases. We are now working on our SLICER foundation.
  
  
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==References==
 
==References==
*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.
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1.  Purdy JA. et. al.; Dose to normal tissue outside the radiation therapy patient's treat volume.; Health Physics 95(5):666-676; 2008.
* 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.
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2.  Burnet NG. et. al.; Defining the tumor and target volumes for radiotherapy; Cancer Imaging (2004) 4, 153-161.
* 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.
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3.  Jena R. et. al.; Diffusion Tensor Imaging: possible implications for radiotherapy treatment planning of patients with high-grade glioma.; Clinical Oncology (2005 17:581-590.
* 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 .
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4.  Wei CW. et. al.; Tumor effects on cerebral white matter as characterized by diffusion tensor tractography; Ca. J. Neurological Sciences 2007; 34:62-69.
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5. Lu S. et. al.; Diffusion-Tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction to the tumor infiltration index.; Radiology 2004 232:221-228.
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6.  Goebell E. et. al.; Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging.; Radiology V239: Number 1 - April 2006, 217-221.
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7. Peng H. et. al.; Development of a human brain diffusion tensor template.; Neuroimage 46 (2009) 967-980.
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</div>
 
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Revision as of 16:33, 22 June 2009

Home < 2009 Summer project week adaptive radiation planning visualization

Instructions for Use of this Template

  1. Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
  2. Copy the entire text of this page into the page created above
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  5. Send an email to tkapur at bwh.harvard.edu if you are stuck

Key Investigators

  • UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
  • Utah: Tom Fletcher, Ross Whitaker

Objective

We are applying functional magnetic resonance imaging, including BOLD and DTI to study the feasibility of adaptive radiation therapy treatment planning and longitudinal tracking/visualization. This is our initial effort and we are concentrating on the application of white matter tractography to improved clinical target volume delineation, tumor characterization and theraputic effects tracking.



Approach, Plan

Our approach will be to add DTI pulse sequences to the end of the standard brain MT protocol on our GE HDx 3T magnet and then, along with the routine tissue density mapping performed in RadOnc using CT, perform parallel dosimetry planning procedures - one using just the standard brain MR protocol and one fusing the added white matter tractography FA maps/reconstructions with the MR and CT images. We expect to observe improved delineation of tumor margins, improvements in treatment responses as measured by standard clinical measures and potential prognostic value for patient signs/symptoms course based on the identified white matter tracks involved.<foo>.

Our plan for the project week is to find out what, if any, DICOM-RT import/export functions are available in slicer and design a composite visualization method able to overlay the results from sequential followup scans as a longitudinal tracking tool.

Progress

Scanning is ready to begin and we are ready to begin accumulating cases. We are now working on our SLICER foundation.


References

1. Purdy JA. et. al.; Dose to normal tissue outside the radiation therapy patient's treat volume.; Health Physics 95(5):666-676; 2008. 2. Burnet NG. et. al.; Defining the tumor and target volumes for radiotherapy; Cancer Imaging (2004) 4, 153-161. 3. Jena R. et. al.; Diffusion Tensor Imaging: possible implications for radiotherapy treatment planning of patients with high-grade glioma.; Clinical Oncology (2005 17:581-590. 4. Wei CW. et. al.; Tumor effects on cerebral white matter as characterized by diffusion tensor tractography; Ca. J. Neurological Sciences 2007; 34:62-69. 5. Lu S. et. al.; Diffusion-Tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction to the tumor infiltration index.; Radiology 2004 232:221-228. 6. Goebell E. et. al.; Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging.; Radiology V239: Number 1 - April 2006, 217-221. 7. Peng H. et. al.; Development of a human brain diffusion tensor template.; Neuroimage 46 (2009) 967-980.