NAMIC Wiki - User contributions [en]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T22:55:14Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finalized the source code
* Finished the DSC analysis module packaging
* Moved repository to QIICR on Github
* Picked up few MGH GBM cases and ran commercial software for comparison
* Updated the Slicer wiki page for DSC analysis module (http://slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DSC_MRI_Analysis)
* Released the module
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

==Source Code==
* [https://github.com/QIICR/DSC_Analysis Source code for analysis of DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T22:54:51Z

Stevedaxiao: /* Project Description */

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finalize the source code
* Finished the DSC analysis module packaging
* Moved repository to QIICR on Github
* Picked up few MGH GBM cases and ran commercial software for comparison
* Updated the Slicer wiki page for DSC analysis module (http://slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DSC_MRI_Analysis)
* Released the module
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

==Source Code==
* [https://github.com/QIICR/DSC_Analysis Source code for analysis of DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T22:51:02Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finished the DSC analysis module packaging
* Moved repository to QIICR on Github
* Picked up few MGH GBM cases and ran commercial software for comparison
* Finalize the source code
* Updated the Slicer wiki page for DSC analysis module (http://slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DSC_MRI_Analysis)
* Released the module
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

==Source Code==
* [https://github.com/QIICR/DSC_Analysis Source code for analysis of DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T22:45:11Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finished the DSC analysis module packaging
* Moved repository to QIICR on Github
* Picked up few MGH GBM cases and ran commercial software for comparison
* Updated the Slicer wiki page for DSC analysis module (http://slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DSC_MRI_Analysis)
* Released the module
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T21:58:07Z

Stevedaxiao: /* Project Description */

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finished the packaging of the DSC analysis module
* Moved repository to QIICR on Github
* Randomly picked few MGH GBM cases and ran commercial software for comparison
* Released the module
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T17:02:23Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

* Finished the packaging of the DSC analysis module
* Randomly picked few MGH GBM cases and ran commercial software for comparison
*
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T16:40:23Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
[[File:DSC_GUI_New.png|300px|thumb|left|GUI of DSC Analysis Module]]
[[File:DSC_Framework_New.png‎|500px|thumb|left|Framework of DSC Analysis]]
[[File:DSC_Comparison.png|600px|thumb|left|Comparison with the Commercial Software]]

{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

*
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

File:DSC Comparison.png

2016-01-07T16:34:29Z

Stevedaxiao:

File:DSC Framework New.png

2016-01-07T16:30:56Z

Stevedaxiao:

File:DSC GUI New.png

2016-01-07T16:27:19Z

Stevedaxiao: GUI of DSC Analysis Module

GUI of DSC Analysis Module

2016 Winter Project Week/Projects/DSCAnalysis

2016-01-07T05:30:21Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

*
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

2016 Winter Project Week/Breakout Sessions/QIICRTools

2016-01-04T16:31:10Z

Stevedaxiao: /* QIICR imaging biomarker analysis tools */

=QIICR Tools=

==Goal==
Give an overview of the various tools from the [http://qiicr.org Quantitative Image Informatics for Cancer Research (QIICR)] project that are available now or are being developed.

==Participants==
The session is aimed at the users and developers interested in the scope of QIICR work. Anyone interested is welcome to attend this session.

Project members expected to attend:
* Andrey Fedorov, BWH
* Jayashree Kalpathy-Cramer, MGH (from 3:30pm)
* Ethan Ulrich, U. Iowa
* Xiao Da, MGH
* Michael Onken, Open Connections GmbH
* Nicole Aucoin, BWH
* Christian Herz, BWH

==Outline==
Slide sets and summary documents:
* [https://goo.gl/MDq0u8 Introduction to QIICR] (slides from CI4CC Faill 2015 symposium)
* [https://figshare.com/articles/Quantitative_imaging_Structured_reporting_in_3D_Slicer_and_beyond/1618826 Quantitative imaging: Structured reporting in 3D Slicer and beyond] (slides from RSNA 2015 course)
* [https://figshare.com/articles/Quantitative_image_analysis_tools_Communicating_quantitative_image_analysis_results/1618827 Quantitative image analysis tools: Communicating quantitative image analysis results] (slides from RSNA 2015 course)
* Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2015) DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ PrePrints 3:e1921 https://doi.org/10.7287/peerj.preprints.1541v1

===QIICR imaging biomarker analysis tools===

* MRI / prostate cancer (Andrey Fedorov)
** DCE MRI modeling
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling
** DWI MRI modeling
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling
** Multiparametric MRI annotation workflow
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/mpReview

* PET/CT / head and neck cancer (Ethan Ulrich)
** PET SUV correction
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PETStandardUptakeValueComputation
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DICOMPETSUVPlugin
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DICOMRWVMPlugin
** PET tumor and hot node segmentation
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/PETTumorSegmentation
** PET quantitative index extraction
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/QuantitativeIndicesTool
** PET analysis workflow (PET-IndiC)
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Extensions/PET-IndiC

* MRI / glioblastoma (Xiao Da)
** DSC MRI modeling
*** http://slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DSC_MRI_Analysis
** DCE MRI modeling
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling
** T1 mapping
*** http://wiki.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1Mapping

===DICOM informatics tools===

===DICOM learning and supporting tools===

===DICOM datasets===

2016 Winter Project Week/Projects/DSCAnalysis

2015-12-30T03:34:10Z

Stevedaxiao: Created page with "__NOTOC__ <gallery> Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|Projects List <!-- Use the "Upload file" link on the left..."

__NOTOC__
<gallery>
Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]

</gallery>

==Key Investigators==


* Xiao Da (MGH)
* Yangming Ou (MGH)
* Andriy Fedorov (BWH)
* Steve Pieper (Isomics)
* Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning. In the context of glial neoplasms, perfusion characteristics have been shown to correlate with tumor type and grade and hence influence treatment decisions.

DSC MRI imaging is based on the principle that flow of a paramagnetic contrast agent through a capillary bed will transiently change the magnetic susceptibility of the given tissue. Decreased signal intensity on spin-echo or gradient-echo images after the first pass of the contrast agent, frequently described as susceptibility-induced T2* shortening, is the result of this temporal change in magnetic susceptibility. This signal time curve is then converted into a concentration time curve, and use of tracer kinetic analysis various hemodynamic variables, such as cerebral blood volume, cerebral blood flow, and mean transit time, as well as metrics that address vessel leakage may be estimated. Combined, these metrics enable microvascular imaging, providing a visual correlate of blood flow, volume, and vessel permeability.
{| class="wikitable"
! style="text-align: left; width:27%" | Objective
! style="text-align: left; width:27%" | Approach and Plan
! style="text-align: left; width:27%" | Progress and Next Steps
|- style="vertical-align:top;"
|

* Package the DSC analysis module
* Make it available as a Slicer extension
|

* Compare the results with the outputs from existing commercial softwares
* Correct some parameters' name and finalize the source code
* Update the source code on Github
* Release the module
|

*
|}

==Background and References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.ncbi.nlm.nih.gov/pubmed/16611779 Leakage correction for DSC MRI]

2016 Winter Project Week

2015-12-30T03:08:48Z

Stevedaxiao:

__NOTOC__
[[image:PW-MIT2016.png|300px]]

'''Dates:''' January 4-8, 2016

'''Location:''' [https://www.google.com/maps/place/MIT:+Computer+Science+and+Artificial+Intelligence+Laboratory/@42.361864,-71.090563,16z/data=!4m2!3m1!1s0x0:0x303ada1e9664dfed?hl=en MIT CSAIL], Cambridge, MA. (Rooms: [[MIT_Project_Week_Rooms#Kiva|Kiva]], R&D)

'''REGISTRATION:''' Register [https://www.regonline.com/namic16 here].
<br>

== Introduction ==
Founded in 2005, the National Alliance for Medical Image Computing (NAMIC), was chartered with building a computational infrastructure to support biomedical research as part of the NIH funded [http://www.ncbcs.org/ NCBC] program. The work of this alliance has resulted in important progress in algorithmic research, an open source medical image computing platform [http://www.slicer.org 3D Slicer], built using [http://www.vtk.org VTK], [http://www.itk.org ITK], [http://www.cmake.org CMake], and [http://www.cdash.org CDash], and the creation of a community of algorithm researchers, biomedical scientists and software engineers who are committed to open science. This community meets twice a year in an event called Project Week.

[[Engineering:Programming_Events|Project Week]] is a semi-annual event which draws 80-120 researchers. As of August 2014, it is a [http://www.miccai.org/organization MICCAI] endorsed event. The participants work collaboratively on open-science solutions for problems that lie on the interfaces of the fields of computer science, mechanical engineering, biomedical engineering, and medicine. In contrast to conventional conferences and workshops the primary focus of the Project Weeks is to make progress in projects (as opposed to reporting about progress). The objective of the Project Weeks is to provide a venue for this community of medical open source software creators. Project Weeks are open to all, are publicly advertised, and are funded through fees paid by the attendees. Participants are encouraged to stay for the entire event.

Project Week activities: Everyone shows up with a project. Some people are working on the platform. Some people are developing algorithms. Some people are applying the tools to their research problems. We begin the week by introducing projects and connecting teams. We end the week by reporting progress. In addition to the ongoing working sessions, breakout sessions are organized ad-hoc on a variety of special topics. These topics include: discussions of software architecture, presentations of new features and approaches and topics such as Image-Guided Therapy.

Several funded projects use the Project Week as a place to convene and collaborate. These include [http://nac.spl.harvard.edu/ NAC], [http://www.ncigt.org/ NCIGT], [http://qiicr.org/ QIICR], and [http://ocairo.technainstitute.com/open-source-software-platforms-and-databases-for-the-adaptive-process/ OCAIRO].

A summary of all previous Project Events is available [[Project_Events#Past|here]].

This project week is an event [[Post-NCBC-2014|endorsed]] by the MICCAI society.

Please make sure that you are on the [http://public.kitware.com/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, January 4
!style="width:18%" |Tuesday, January 5
!style="width:18%" |Wednesday, January 6
!style="width:18%" |Thursday, January 7
!style="width:18%" |Friday, January 8
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9:00am-12:00pm'''
|'''10:30am-12pm:''' [Tutorial] Diffeomorphic registration and geodesic shooting methods (I). (Sarang Joshi)<br> Room: [http://www.csail.mit.edu/resources/maps/5D/D507.gif 32-D507].
|'''10:00-11:30am:''' Breakout Session:[[2016_Winter_Project_Week/Breakout_Sessions/NewSlicerExtensions | Slicer Extensions Birds of a Feather]]
|
'''10:00-11:30am:''' Breakout Session: [[2016_Winter_Project_Week/Projects/SlicerROSIntegration| Slicer for Medical Robotics Research]]
<br>
|'''8:30-9:30am''' TBD <br>
'''9:30-10:30am''' [[2016_Winter_Project_Week/Breakout_Sessions/IGT#Image-guided Neurosurgery| Clinical perspective on Image Guided Neurosurgery]] (Alexandra Golby) <br>
'''10:30-11:30am''' [[2016_Winter_Project_Week/Breakout_Sessions/IGT#Multiparametric MRI| Clinical perspective on Multiparametric MRI]] (Fiona Fennessy)<br>
'''11:30am-12:30pm''' TBD <br>
|'''10:00am-12:00pm:''' [[#Projects|Project Progress Updates]]<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
<br>-----------------<br>
'''12pm:''' [[Events:TutorialContestJanuary2016|Tutorial Contest Winner Announcement]]<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
|-
|bgcolor="#ffffdd"|'''12:00pm-1:00pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1:00-5:30pm'''
|'''1:00pm-1:05pm: <font color="#503020">Welcome</font>'''<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
<br>-----------------<br>
'''1:05-2:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
<br>-----------------<br>
'''2:45-4:00pm:''' Breakout Session: [[2016_Winter_Project_Week/Breakout_Sessions/Ultrasound| Ultrasound]]<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
<br>-----------------<br>

'''4:00-5:30pm:''' [Tutorial] Diffeomorphic registration geodesic shooting methods (II). (Sarang Joshi) <br> Room: [http://www.csail.mit.edu/resources/maps/5D/D507.gif 32-D507].
|
|'''1:00-2:30pm:''' Breakout Session:[[2016_Winter_Project_Week/Breakout_Sessions/DiffusionMRI| Diffusion MRI]]<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]] <br>
'''3:00-4:30pm:''' Breakout Session:[[2016_Winter_Project_Week/Breakout_Sessions/QIICRTools| QIICR Tools]]
|'''1:00-3:00pm:''' Breakout Session:[[2016_Winter_Project_Week/Breakout_Session/What's_Planned_for_Slicer_Core|What's Planned for Slicer Core]]<br>
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

==Calendar==
{{#widget:Google Calendar
|id=kitware.com_sb07i171olac9aavh46ir495c4@group.calendar.google.com
|timezone=America/New_York&dates=20160103%2F20160110
|title=NAMIC Winter Project Week
|view=WEEK
|dates=20160103/20160110
}}

iCal (.ics) link: https://calendar.google.com/calendar/ical/kitware.com_sb07i171olac9aavh46ir495c4%40group.calendar.google.com/public/basic.ics

='''Projects'''=
*Use this [[2016_Project_Week_Template | Updated Template for project pages]]

== Tractography==
* [[2016_Winter_Project_Week/Projects/Tractography_format_interoperability | Tractography Format Interoperability]] (Isaiah Norton, Michael Onken, Lauren O'Donnell, others)
* [[2016_Winter_Project_Week/Projects/SlicerDMRI_documentation | Slicer Diffusion MR / tractography workflow documentation]] (Pegah Kahaliardabili, Fan Zhang, Isaiah Norton, Lauren O'Donnell, others)
* [[2016_Winter_Project_Week/Projects/TractographyModuleDevelop&Test | Tractography Analysis Module Development and Testing]] (Fan Zhang, Pegah Kahaliardabili, Isaiah Norton, Lauren O'Donnell, others)

== IGT ==
* [[2016_Winter_Project_Week/Projects/TrackedUltrasoundStandardization | Tracked Ultrasound Standardization]] (Andras Lasso, Christian Askeland, Simon Drouin, Junichi Tokuda, Steve Pieper, Adam Rankin)
*[[2016_Winter_Project_Week/Projects/IntegrationCustusX|Integration of CustusX with PLUS on BK System]] (Christian A, Andras Lasso, Adam Rankin)
*[[2016_Winter_Project_Week/Projects/MITK_Plus_Integration | Integration of Plus and MITK]] (Thomas Kirchner, Janek Groehl)
*[[2016_Winter_Project_Week/Projects/IntegrationImFusion| Integration of ImFusion MR-US Registration with BWH AMIGO Neurosurgery Setup]] (Sarah Frisken, Tina Kapur, Steve Pieper, Sandy Wells, Andras Lasso, Christian Askelan)
* [[2016_Winter_Project_Week/Projects/SlicerROSIntegration | 3D Slicer + ROS Integration]] (Junichi Tokuda, Axel Krieger, Simon Leonard, Jayender Jagadeesan)
* [[2016_Winter_Project_Week/Projects/CryoPlanningSlicerModule | CryoPlanning Module in Slicer]] (Jayender Jagadeesan, Steve Pieper, Sandy Wells)
* [[2016_Winter_Project_Week/Projects/External_beam_planning | External Beam Radiotherapy Planning]] (Greg Sharp, others)
* [[2016_Winter_Project_Week/Projects/EVD |Measuring Anatomic Factors for Extraventricular Drain Placement]] (Kirby Vosburgh, P. Jason White)
* [[2016_Winter_Project_Week/Projects/PLUS | Inter-device messaging for robust support of depth switching]] (Adam Rankin)
* [[2016_Winter_Project_Week/Projects/PLUSOCR | Exploration of open-source OCR libraries for device meta-data capture without research interface ]] (Adam Rankin)

==Image Analysis==
*[[2016_Winter_Project_Week/Projects/ChestImagingPlatform|Chest Imaging Platform: COPD and Other Pulmonary Diseases]] (Raúl San José, Jorge Onieva)
* [[2016 Winter Project Week/Projects/Cluster-Driven Lung Segmentation | Cluster-Driven Segmentation of Lung Nodules]] (Vivek Narayan, Raúl San José, Daniel Blezek, Steve Pieper, Chintan Parmar)
* [[2016_Winter_Project_Week/Projects/BatchImageAnalysis | Batch Clinical Image Analysis]] (Kalli Retzepi, Yangming Ou, Matt Toews, Steve Pieper, Sandy Wells, Randy Gollub)
* [[2016_Winter_Project_Week/Projects/ImageRestoration | Image Restoration via Patch GMMs]] (Adrian Dalca, Katie Bouman, Polina Golland)
* [[2016_Winter_Project_Week/Projects/PatchRegistration | Patch Based Discrete Registration for Difficult Images]] (Adrian Dalca, Andreea Bobu, Polina Golland)
* [[2016_Winter_Project_Week/Projects/DigitalPathologyNuclearSegmentation|Digital Pathology Nuclear Segmentation]] (Erich Bremer, Yi Gao, Nicole Aucoin, Andrey Fedorov)
* [[2016_Winter_Project_Week/Projects/SphericalWaveletShapeAnalysis|Spherical Wavelet Shape Analysis]] (Yi Gao, Erich Bremer, Allen Tannenbaum, Ron Kikinis)
* [[2016_Winter_Project_Week/Projects/Interactive4DSegmentation | Interactive 4D Segmentation Module]] (Ethan Ulrich)
* [[2016_Winter_Project_Week/Projects/SlicerCMFNextSteps | Moving beyond SlicerCMF and Future Projects]] (Beatriz Paniagua, Lucia Cevidanes, Steve Pieper, Juan Prieto)
* [[2016_Winter_Project_Week/Projects/SlicerOpenCVExtension | Slicer OpenCV Extension]] (Nicole Aucoin, Erich Bremer, Andrey Fedorov)
* [[2016_Winter_Project_Week/Projects/ShapeAnalysis | Low-dimensional Principal Geodesic Analysis On the Manifold of Diffeomorphisms]] (Miaomiao Zhang, Polina Golland)
* [[2016_Winter_Project_Week/Projects/DSCAnalysis | Dynamic Susceptibility Contrast (DSC) MRI Analysis]] (Xiao Da, Yangming Ou, Andriy Fedorov, Steve Pieper, Jayashree Kalpathy-Cramer)

==Infrastructure==
*[[2016_Winter_Project_Week/Projects/UpgradeNAMICSlicerWiki|Upgrade the NAMIC (and Slicer?) Wiki]] (Mike Halle, JC)
* [[2016_Winter_Project_Week/Projects/CommonDataStructure | Common Data Structure for CMF modules in Slicer]] (Jean-Baptiste Vimort, François Budin, Lucia Cevidanes, Beatriz Paniagua, Steve Pieper, Juan Prieto)
* [[2016_Winter_Project_Week/Projects/StatisticalShapeModeling | Statistical Shape Modeling in Slicer: OA Index]] (Laura Pascal, Beatriz Paniagua, François Budin, Lucia Cevidanes, Steve Pieper, Juan Prieto)
* [[2016_Winter_Project_Week/Projects/CommonGL | CommonGL]] (Steve Pieper, Jim Miller)
* [[2016_Winter_Project_Week/Projects/CLIModules Backgrounding in MeVisLab | Running CLI Modules in MeVisLab Asynchronously]] (Hans Meine)
* [[2016_Winter_Project_Week/Projects/BRAINSFit_in_MeVisLab | Interoperability Tests with BRAINSFit (or other interesting CLIs) in MeVisLab]] (Hans Meine, Steve Pieper)
* [[2016_Winter_Project_Week/Projects/CLI_Dashboard | Kibana Dashboard for Browsing All Available CLI Modules]] (Hans Meine, JC?)
* [[2016_Winter_Project_Week/Projects/SegmentationEditorWidget | Editor Widget using Segmentations]] (Csaba Pinter, Andras Lasso, Andrey Fedorov, Steve Pieper?)
* [[2016_Winter_Project_Week/Projects/SlicerTerminologyEditor | Terminology Editor]] (Csaba Pinter, Nicole Aucoin, Andrey Fedorov)
* [[2016_Winter_Project_Week/Projects/DICOMSegObjIntegration | Integration of DICOM Segmentation Image Storage with Segmentations Module]] (Kyle Sunderland, Csaba Pinter, Andras Lasso, Andrey Fedorov, Steve Pieper?)
* [[2016_Winter_Project_Week/Projects/CondaSlicer | Integration of Anaconda Python in Slicer]] (JC, Raúl San José, Jorge Onieva, Slicer Community?)
* [[2016_Winter_Project_Week/Projects/Data Persisting | Mechanism to Persist Clinical User Data from Different Modules Based on SQLite and/or other Database Engines ]] (Raúl San José, Jorge Onieva)
* [[2016_Winter_Project_Week/Projects/Workflows | Workflow Module that Enables the Navigation and Data Sharing between Different Modules in a Clinical Workflow ]] (Raúl San José, Jorge Onieva)
* [[2016_Winter_Project_Week/Projects/AIMInteroperability | AIM for Interoperability]] (Hans Meine, Andrey Fedorov, ??)
* [[2016_Winter_Project_Week/Projects/3DNrrdSequences | Sequences extension support for 3D+t NRRD]] (Adam Rankin)
* [[2016_Winter_Project_Week/Projects/SlicerEnhancedMR | Developing support for Enhanced MR in Slicer]] (Michael Onken, Andrey Fedorov)

= '''Logistics''' =

*'''Dates:''' January 4-8, 2016
*'''Location:''' MIT, Kiva Conference room; 4th floor of Building 32.
*'''REGISTRATION:''' Register [https://www.regonline.com/namic16 here]. Registration Fee: $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list [[2016_Winter_Project_Week/RoomSharing|here]]

= '''Registrants''' =

Do not add your name to this list - it is maintained by the organizers based on your paid registration. To register, visit this [https://www.regonline.com/namic16 registration site].

#Polina Golland, MIT
#Ron Kikinis, BWH
#Nicole Aucoin, BWH/SPL
#Peter Anderson
#Daniel Blezek, Isomics, Inc.
#Lucia Cevidanes, University of Michigan
#Adrian Dalca, MIT
#Simon Drouin, Montreal Neurological Institute
#Janek Groehl, German Cancer Research Center
#Tina Kapur, BWH/HMS
#Thomas Kirchner, German Cancer Research Center
#Hans Meine, University of Bremen/MEVIS
#Vivek Narayan, Dana Farber Cancer Institute
#Danielle Pace, MIT
#Laura Pascal, University of Michigan
#Steve Pieper, Isomics, Inc.
#Csaba Pinter, Queen's University
#Gregory Sharp, MGH
#James Miller, GE Research
#Kyle Sunderland, Queen's University
#Ethan Ulrich, University of Iowa
#Jean-Baptiste Vimort, University of Michigan
#Miaomiao Zhang, MIT
#Beatrize Paniagua, University of North Carolina at Chapel Hill
#Sonia Pujol, BWH
#Junichi Tokuda, BWH
#Katie Mastrogiacomo, BWH
#Niravkumar Patel, Worcester Polytechnic Institute
#Michael Onken, Open Connections (Germany)
#Erich Bremer, Stony Brook University
#Xiao Da, MGH
#Tobias Frank, Leibniz Universität Hannover
#Kirby Vosburgh, BWH
#P. Jason White, BWH
#Lauren O'Donnell, BWH
#Pegah Kahali, BWH
#Fan Zhang, BWH
#Adam Rankin, Robarts Research Institute
#Simon Leoard, Johns Hopkins University
#David Gering, HealthMyne
#Johan Andruejol, Kitware
#Jean-Christophe Fillion-Robin, Kitware
#Kelly Xu, MIT
#Christian Askeland, SINTEF
#Katharine Carter, BWH
#Nick Todd, BWH
#Ye Cheng, BWH
#Andriy Fedorov, BWH/HMS
#Sudhanshu Semwal, UCCS Professor
#Michael Halle, BWH
#Kallirroi Retzepi, MGH
#Jayender Jagadeesan, BWH
#Nathalie Agar, BWH
#Curtis Lisle, KnowledgeVis, LLC
#Andras Lasso, PerkLab, Queen's University
#Sarah Frisken, BWH
#Yi Gao, Stony Brook University
#Christian Herz, BWH
#Prashin Unadkat, SPL/BWH

2015 Summer Project Week:T1 mapping

2015-07-15T16:13:51Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Provide users with options to use ROI mask and choose which flip angles to include or exclude for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on [https://github.com/stevedaxiao/T1_Mapping_CPP.git Github]
</div>
</div>

==References==

* [https://sites.duke.edu/dblab/qibacontent/ QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:DSC

2015-06-24T11:33:40Z

Stevedaxiao: /* References */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options include output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:DSC

2015-06-24T11:30:05Z

Stevedaxiao: /* References */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options include output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/ Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/ Other method to investigate DSC MRI]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:DSC

2015-06-24T11:27:51Z

Stevedaxiao: /* References */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options include output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/pdf/radiol.2492071659.pdf Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:DSC

2015-06-24T11:24:20Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options include output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:DSC

2015-06-24T11:19:25Z

Stevedaxiao: /* References */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options includes output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208985/pdf/nihms596683.pdf Other method to investigate DSC MRI]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:DSC

2015-06-24T11:16:01Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative Cerebral Blood Volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative Cerebral Blood Volume (rCBV) map, Relative Cerebral Blood Flow (rCBF) map, Mean Transit Time (MTT)and etc.
** Additional Options includes output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]
*

2015 Summer Project Week:DSC

2015-06-24T11:11:24Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:DSC_logo.png|DSC MRI Analysis Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for conversion of signal intensity to concentration for DSC MRI
* Updated the basic equations for computing DSC parametric maps (eg, Relative cerebral blood volume (rCBV) map)
* Discussion about loading DSC MRI Dicoms to Slicer as multi-volume 4D data
** Read the ''Dicom to Nifti Conversion Guide of Dicom unpack for Freesurfer''[http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack]
** The tag for DSC MRI Dicoms to identify different volumes is different from DCE MRI Dicoms
** Number of Volumes (ie, number of frames or time points) is the number of files with the same image position
** Time between volumes/frames is the repetition time (TR)
** Still debugging the code
* Created a module for DSC MRI analysis
** Take multi-volume DSC images as input
** Read Dicom header information automatically
** Provide users with options to use population AIF, ROI mask, AIF mask and prescribed AIF
** Output DSC parametric maps including Relative cerebral blood volume (rCBV) map, Relative cerebral blood flow (rCBF) map, Mean transit time (MTT)and etc.
** Additional Options includes using constant BAT and output concentration map, fitted map and etc.
* Did some tests on sample data from MGH
* Uploaded the source code on [https://github.com/stevedaxiao/DSC_Analysis.git Github]

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]
*

2015 Summer Project Week:DSC

2015-06-24T10:29:35Z

Stevedaxiao: /* */

2015 Summer Project Week:DSC

2015-06-24T10:24:24Z

Stevedaxiao:

File:DSC logo.png

2015-06-24T10:22:54Z

Stevedaxiao:

2015 Summer Project Week:T1 mapping

2015-06-24T10:08:33Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Provide users with options to use ROI mask and choose which flip angles to include or exclude for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on [https://github.com/stevedaxiao/T1_Mapping_CPP.git Github]
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T10:03:28Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Allow users to choose which flip angles will be included or excluded for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on [https://github.com/stevedaxiao/T1_Mapping_CPP.git Github]
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T10:00:52Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equations for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Allow user to choose which flip angles will be included or excluded for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on [https://github.com/stevedaxiao/T1_Mapping_CPP.git Github]
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T09:59:48Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equation for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Allow user to choose which flip angles will be included or excluded for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on [https://github.com/stevedaxiao/T1_Mapping_CPP.git Github]
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T09:59:12Z

Stevedaxiao: /* */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer module using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with the ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Updated the equation for variable flip angle (VFA) T1 mapping
* Compared the results using C++ with Python, Freesurfer and the ground truth of T1 for QIBA phantom data
* T1 mapping results on QIBA phantom data using C++ are comparable with the ground truth and the results using Freesurfer and Python
* Did some tests on MGH Brain Tumor MR Data with multiple flip angles
* Created a module for T1 mapping using C++
** Take multi-spectral FLASH images with an arbitrary number of flip angles as input, and estimate the T1 values of the data for each voxel
** Read repetition time(TR), echo time(TE) and flip angles from the Dicom header automatically
** Allow user to choose which flip angles will be included or excluded for the fitting process
** Output the fitting volume and quality of fitting image as well
* Uploaded the source code on Github
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:DSC

2015-06-24T09:38:37Z

Stevedaxiao: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|300px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations for conversion of signal to concentration for DSC have already been updated
* Working on the parametric maps computation and DSC Dicom conversion

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:T1 mapping

2015-06-24T09:38:08Z

Stevedaxiao: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|400px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:DSC

2015-06-24T09:36:07Z

Stevedaxiao: /* Project Description */

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|350px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|450px|thumb|left|Framework to Compute DSC Parametric Maps]]

== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations for conversion of signal to concentration for DSC have already been updated
* Working on the parametric maps computation and DSC Dicom conversion

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:T1 mapping

2015-06-24T09:35:11Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|350px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|450px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|350px|thumb|left|Sample Results of T1 Mapping]]
== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:DSC

2015-06-24T09:32:55Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
[[File:DSC_GUI.png|250px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]
== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations for conversion of signal to concentration for DSC have already been updated
* Working on the parametric maps computation and DSC Dicom conversion

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

2015 Summer Project Week:T1 mapping

2015-06-24T09:32:05Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
[[File:T1_Mapping_CPP_GUI.png|250px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|300px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|250px|thumb|left|Sample Results of T1 Mapping]]
== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T09:31:39Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.

[[File:T1_Mapping_CPP_GUI.png|250px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|300px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|250px|thumb|left|Sample Results of T1 Mapping]]
== ==
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:DSC

2015-06-24T09:24:20Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
</gallery>

[[File:DSC_GUI.png|250px|thumb|left|DSC MRI Analysis GUI]]
[[File:DSC_Result_updated.png‎|400px|thumb|left|Framework to Compute DSC Parametric Maps]]

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Steve Pieper (Isomics), Jayashree Kalpathy-Cramer (MGH)

==Project Description==
Dynamic Susceptibility Contrast (DSC) MRI imaging is an important functional imaging method that enables quantitative assessment of tissue hemodynamic patterns. Abnormality of blood flow, volume and permeability is frequently observed during tumor growth, and characterization of these perfusion attributes has become clinically important for both diagnosis and therapy planning.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Create a module for the analysis of Dynamic Susceptibility Contrast (DSC) MRI
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/PkModeling Slicer PKmodule]
* Find the DICOM tag to identify DSC different time frame and load them as 4D data
* Update equations for conversion of signal to concentration for DSC
* Compute DSC parametric maps(eg, rCBV)
* Test some sample cases from MGH
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations for conversion of signal to concentration for DSC have already been updated
* Working on the parametric maps computation and DSC Dicom conversion

</div>
</div>

==References==

* [http://www.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/siemens_hwem-hwem_ssxa_websites-context-root/wcm/idc/groups/public/@global/@imaging/@mri/documents/download/mdaw/mtix/~edisp/brain_perfusion_how_why-00093544.pdf Introduction of DSC MRI from Siemens]
* [http://www.nmr.mgh.harvard.edu/~greve/dicom-unpack Dicom unpack for Freesurfer]
* [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2657863/?report=reader Equations for conversion of signal to concentration for DSC]
* [https://github.com/stevedaxiao/DSC_Analysis.git Source code for analysis of DSC MRI]

File:DSC Result updated.png

2015-06-24T09:21:42Z

Stevedaxiao:

File:DSC Result.png

2015-06-24T09:17:04Z

Stevedaxiao: uploaded a new version of "File:DSC Result.png"

2015 Summer Project Week:T1 mapping

2015-06-24T09:15:33Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

[[File:T1_Mapping_CPP_GUI.png|250px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|300px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|250px|thumb|left|Sample Results of T1 Mapping]]

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T09:14:37Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

[[File:T1_Mapping_CPP_GUI.png|200px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|200px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|200px|thumb|left|Sample Results of T1 Mapping]]

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T09:12:32Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
</gallery>

[[File:T1_Mapping_CPP_GUI.png|300px|thumb|left|T1 Mapping C++ GUI]]
[[File:Comparison_T1_Mapping_ALL.png‎|300px|thumb|left|Comparison of Different T1 Mapping Tools]]
[[File:T1_Mapping_Result_Sample.png|300px|thumb|left|Sample Results of T1 Mapping]]

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

2015 Summer Project Week:T1 mapping

2015-06-24T08:52:19Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
Image:T1_Mapping_CPP_GUI.png|T1 Mapping C++ GUI
Image:Comparison_T1_Mapping_ALL.png|Comparison of Different T1 Mapping Tools
Image:T1_Mapping_Result_Sample.png|Sample Results of T1 Mapping
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

File:T1 Mapping Result Sample.png

2015-06-24T08:49:48Z

Stevedaxiao:

2015 Summer Project Week:T1 mapping

2015-06-23T23:52:20Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_logo.png|T1 Mapping Logo
Image:T1_Mapping_CPP_GUI.png|T1 Mapping C++ GUI
Image:Comparison_T1_Mapping_ALL.png|Comparison of Different T1 Mapping Tools
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

File:T1 Mapping logo.png

2015-06-23T23:51:05Z

Stevedaxiao:

2015 Summer Project Week:T1 mapping

2015-06-23T23:49:47Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_CPP_GUI.png|T1 Mapping C++ GUI
Image:Comparison_T1_Mapping_ALL.png|Comparison of Different T1 Mapping Tools
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]

File:Comparison T1 Mapping ALL.png

2015-06-23T23:47:37Z

Stevedaxiao:

2015 Summer Project Week:T1 mapping

2015-06-23T23:46:46Z

Stevedaxiao:

__NOTOC__
<gallery>
Image:PW-Summer2015.png|[[2015_Summer_Project_Week#Projects|Projects List]]
Image:T1_Mapping_CPP_GUI.png|T1 Mapping C++ GUI
</gallery>

==Key Investigators==
* Xiao Da (MGH), Yangming Ou (MGH), Andriy Fedorov (BWH), Jayashree Kalpathy-Cramer (MGH)
* Utsav Pardasani (Observing)

==Project Description==
T1 mapping estimates effective tissue parameter maps (T1) from multi-spectral FLASH MRI scans with different flip angles. T1 mapping can be used to optimize parameters for a sequence, monitor diseased tissue, measure Ktrans in DCE-MRI and etc.
<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
* Estimate effective T1 from multi-spectral FLASH MRI scans with different flip angles
* Implement T1 mapping algorithm as a Slicer extension using C++
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Start with [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/DWModeling prostate diffusion module]
* Update equations for T1 mapping
* Compare the results using C++, [http://www.slicer.org/slicerWiki/index.php/Documentation/Nightly/Modules/T1_Mapping Python], [https://surfer.nmr.mgh.harvard.edu/fswiki/mri_ms_fitparms Freesurfer ] with ground truth of T1 for [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA phantom]
* Test the Slicer module on MGH Brain Tumor MR Data with multiple flip angles

</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Equations have already been updated
* Working on the GUI
* Working on the comparison
</div>
</div>

==References==

* [https://dblab.duhs.duke.edu/modules/QIBAcontent/index.php?id=1 QIBA T1 phantom]
* [http://europepmc.org/articles/pmc3620726 Basic equations for T1 Mapping]
* [https://github.com/stevedaxiao/T1_Mapping.git Source code for T1 Mapping Python Version]
* [https://github.com/stevedaxiao/T1_Mapping_CPP.git Source code for T1 Mapping C++ Version]