NAMIC Wiki - User contributions [en]

Algorithm:MGH

2007-11-29T00:06:23Z

Nicks: /* Numerical Recipies Replacement */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:qdec.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:nrrd256.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:vxl.gif|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our objective is to replace algorithms using the proprietary Numerical Recipes for C source base in FreeSurfer in the efforts to open-source FreeSurfer. This project has been completed through the use of the open source packages VXL (VNL) and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 25 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:histo_matching.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

File:Vxl.gif

2007-11-28T23:54:34Z

Nicks:

File:Nrrd256.jpg

2007-11-28T23:54:10Z

Nicks:

Algorithm:MGH

2007-11-28T23:53:54Z

Nicks:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:qdec.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:nrrd256.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:vxl.gif|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our objective is to replace algorithms using the proprietary Numerical Recipes for C source base in FreeSurfer in the efforts to open-source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 25 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:histo_matching.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

Algorithm:MGH

2007-11-28T23:51:50Z

Nicks: /* Numerical Recipies Replacement */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:qdec.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our objective is to replace algorithms using the proprietary Numerical Recipes for C source base in FreeSurfer in the efforts to open-source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 25 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:vxl.gif|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

[[Image:histo_matching.jpg|thumb|left|200px]]
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

File:Overcomplete vs biorthogonal wavelets.jpg

2007-11-28T23:48:54Z

Nicks:

Algorithm:MGH

2007-11-28T23:48:44Z

Nicks:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:qdec.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 10 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

[[Image:histo_matching.jpg|thumb|left|200px]]
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

Algorithm:MGH

2007-11-28T23:40:39Z

Nicks:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:placeholder.png|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:qdec.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 10 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

[[Image:histo_matching.jpg|thumb|left|200px]]
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

File:Qdec.jpg

2007-11-28T23:30:17Z

Nicks:

Algorithm:MGH

2007-11-28T23:24:30Z

Nicks: /* QDEC: An easy to use GUI for group morphometry studies */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:placeholder.png|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:qdec.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.tif|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 10 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

[[Image:histo_matching.jpg|thumb|left|200px]]
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

Algorithm:MGH

2007-11-28T23:23:31Z

Nicks: /* QDEC: An easy to use GUI for group morphometry studies */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MGH Algorithms =

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

= MGH Projects =

{|
| style="width:10%" | [[Image:placeholder.png|left|200px]]
| style="width:90%" |

== [[Algorithm:MGH:QDEC|QDEC: An easy to use GUI for group morphometry studies]] ==

'''New: ''' Qdec is a application included in the Freesurfer software package intended to aid researchers in performing inter-subject / group averaging and inference on the morphometry data (cortical surface and volume) produced by the Freesurfer processing stream. The functionality in Qdec is also available as a processing module within Slicer3, and XNAT.[[Algorithm:MGH:QDEC|More...]]

See: [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

|-

| | [[Image:qdec.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==

Our objective is to open a NRRD volume in FreeSurfer, and convert an MGH volume to a NRRD volume with Freesurfer. This project allows the seemless exchange of diffusion-based volumetric data between Slicer and the FreeSurfer analysis stream, including tensors, eigendirections, as well as raw muli-direction diffusion data.
[[Algorithm:MGH:NRDDFreesurfer]]

== [[Algorithm:MGH:Development:SphericalWavelets|Spherical Wavelets]] ==
Cortical Surface Shape Analysis Based on Spherical Wavelets. We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been proved to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant to rotation of the underlying surface parameterization. In this paper, we demonstrate the theoretical advantage of over-complete wavelets over bi-orthogonal wavelets and illustrate their utility on both synthetic and real data. In particular, we show that the over-complete spherical wavelet transform enjoys significant advantages for the analysis of cortical folding development in a newborn dataset.'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

|-

| | [[Image:overcomplete_vs_biorthogonal_wavelets.tif|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:TopologyCorrection|Topology Correction]] ==
Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops. We propose a technique to accurately correct
the spherical topology of cortical surfaces. Specifically,we construct
a mapping from the original surface onto the sphere to detect
topological defects as minimal nonhomeomorphic regions. The
topology of each defect is then corrected by opening and sealing
the surface along a set of nonseparating loops that are selected in
a Bayesian framework. The proposed method is a wholly self-contained
topology correction algorithm, which determines geometrically
accurate, topologically correct solutions based on the magnetic
resonance imaging (MRI) intensity profile and the expected
local curvature. Applied to real data, our method provides topological
corrections similar to those made by a trained operator.
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

 

|-

| | [[Image:separating_loops.jpg|thumb|left|200px]]
| |

== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==

Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. This project has been completed through the use of the open source packages VNL and Cephes. This includes the complete replacement of all Numerical Recipes in C code, and the implementation of a battery of unit tests for each replaced function. Currently the open source release is at a beta stage, and 10 beta releases of the source have been made. We anticipate a complete open source release in first quarter 2008.

'''New: ''' Completed

|-

| | [[Image:placeholder.png|thumb|left|200px]]
| |

== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]

[[Image:histo_matching.jpg|thumb|left|200px]]
'''New: ''' IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 4, APRIL 2007

People

2007-11-21T21:41:42Z

Nicks: Dennis Jen no longer at MGH

== Personnel at least partially funded by NA-MIC ==

#Leadership
## [[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)
## Tina Kapur, Harvard (BWH SPL)
## Nicole Aucoin, Harvard (BWH SPL)
## Wendy Plesniak, Harvard (BWH SPL)
## Marianna Jakab, Harvard (BWH SPL)
# Algorithms Core
## [http://www.cs.utah.edu/~whitaker/ Ross Whitaker], Utah PI
## [[Eric_Grimson|Eric Grimson]], MIT
## [[Polina_Golland|Polina Golland]], MIT PI
## [[User:Styner|Martin Styner]], UNC PI
## Ipek Oguz, UNC
## Gary Long, UNC
## Allen Tannenbaum, Georgia Tech, PI
## Oleg Michailovich, Georgia Tech
## Preston T. Fletcher, Utah
## Ran Tao, Utah
## [http://www.cs.unc.edu/~gerig/ Guido Gerig], UTAH
## [[User:Gcasey|Casey Goodlett]], UTAH (Guido)
## [http://www.massgeneral.org/ncs/neuro_faculty_Kennedy.htm/ David Kennedy], Ph.D, Harvard (MGH) PI
## Bruce Fischl, Ph.D, Harvard (MGH)
## [[User:nicks|Nick Schmansky]], Harvard (MGH)
## Daniel Marcus, Washington Univerity at St. Louis
# Engineering Core
## [[User:Lorensen|Will Schroeder]], Kitware PI
## [[User:Ibanez|Luis Ibanez]], Kitware
## [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman], Kitware
## [[User:Barre|Sebastien Barre]], Kitware
## [[User:Millerjv|Jim Miller]], GE PI
## [[User:Taox|Xiaodong Tao]], GE
## Dan Blezek, GE
## [[User:Pieper|Steve Pieper]], Isomics PI
## Alex Yarmakovich, Isomics
## Arthur W. Toga, UCLA PI
## Jia Wei Tam, UCLA
## [[User:Jags|Jagadeeswaran Rajendiran]], UCLA
## Mark Ellisman, UCSD PI
## Jeff Grethe, UCSD
## [http://www.cse.ucsd.edu/~ncjones/bio.html/ Neil Jones], UCSD
## Bryan W. Smith, UCSD Supplement
# Driving Biological Problems (DBP)
## [http://www.cisst.org/~gabor/ Gabor Fichtinger], Queen's
## [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi], Queen's
## [http://imaging.robarts.ca/~dgobbi/ David Gobbi], Queen's
## Jonathan Boisvert, Queen's
## Jeremy Bockholt, The Mind Institute
## Chuck Gasparovic, The Mind Institute
## [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett], UNC
## Clement Vachet, UNC
## Gary Long, UNC
## Rachel Gimpel Smith, UNC
## [http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], BWH
## Usman Khan, BWH
## Marc Niethammer, BWH
## Katharina Quintus, BWH
# Service
## [[User:Will|Will Schroeder]], Kitware PI
## Ken Martin, Kitware
## Brad Davis, Kitware
# Training
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI
## Guido Gerig, UNC
## [[User:Whitaker]], Utah
## Martha Shenton, Harvard (BWH PNL) PI
## [[User:SPujol|Sonia Pujol]], Harvard (BWH SPL)
# Dissemination
## [[User:Tkapur|Tina Kapur]], BWH SPL co-PI
## [[User:Pieper|Steve Pieper]], Isomics co-PI
# Management
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)
## [[User:Rachana|Rachana Manandhar]], Harvard (BWH SPL)

 

== NA-MIC Collaborators ==
These NA-MIC collaborators are funded under the "Collaboration with NCBC" PAR.
#Nicole Grosland, UIowa
#Vincent Magnotta, UIowa
#Steve Pieper, Isomics
#James Daunais, Wakeforest
#Robert Kraft, Wakeforest
#Chris Wyatt, VT
#Kilian Pohl, BWH
#Sandy Wells, BWH
#Kevin Cleary, Georgetown
#Enrique Campos-Nanez, Georgetown
#Patrick (Peng) Cheng, Georgetown
#Ziv Yaniv, Georgetown
#Nobuhiko Hata, BWH

== NA-MIC alumni ==
* [http://marchingcubes.org Bill Lorensen]
* [[User:Lzollei|Lilla Zollei]], MIT
* Lauren O'Donnell, MIT
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT
* Ramsey Al-Hakim, Georgia Tech
* [[User:Melonakos|John Melonakos]], Georgia Tech
* [[User:Lankton|Shawn Lankton]], Georgia Tech
* [[User:Nain|Delphine Nain]], Georgia Tech
* Xavier Le Faucheur, Georgia Tech
* [[User:Mohan|Vandana Mohan]], Georgia Tech
* Tom Fletcher, Utah
* [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah
* [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah
* Josh Snyder, Harvard (MGH)
* [[User:DavidTuch|David Tuch]], Harvard (MGH)
* [[User:Karthik|Karthik Krishnan]],Kitware
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware
* [[User:Mathieu|Mathieu Malaterre]], Kitware
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA
* [[User:MichaelPan|Michael Pan]], UCLA
* Brendan Flaherty, UCSD
* [[User:Adamc|Adam Cohen]] Harvard (BWH PNL)
* [[User:Markd|Mark Dreusicke]] Harvard (BWH PNL)
* Martha Shenton, Harvard (BWH PNL) PI
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI
* Bob Roth, Dartmouth
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth
* Alan Green, Dartmouth
* John West, Dartmouth
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth
* Stephen Guerin, Dartmouth
* John MacDonald, Dartmouth
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI
* [[User:Jfallon|James Fallon]], UCI
* Jessica Turner, UCI
* Martina Panzenboeck, UCI
* David Medina, UCI
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI
* Diane Highum, UCI
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI
* Liv Trondsen, UCI
* Fabio Macciardi, Toronto
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto
* Aristotle Voineskos, Toronto

== "Friends and Family" ==

=== NIH ===

* [[User:Lysterp|Peter M. Lyster]]

=== mBIRN ===

* [[User:Akolasny|Anthony Kolasny]]
* [[User:Dmarcus|Dan Marcus]]
* [[User:Kikinis|Ron Kikinis]]

=== fBIRN ===

* [[User:Kikinis|Ron Kikinis]]

=== IGT ===

* [[User:Ibanez|Luis Ibanez]]
* [http://wiki.na-mic.org/Wiki/index.php/Slicer-IGT Nobuhiko Hata]

=== Other ===

People

2007-11-21T21:40:27Z

Nicks: added link to user page

== Personnel at least partially funded by NA-MIC ==

#Leadership
## [[User:Kikinis|Ron Kikinis]], Harvard (BWH SPL) PI
## [[User:Mastrogiacom|Katie Mastrogiacomo]], Harvard (BWH SPL)
## Tina Kapur, Harvard (BWH SPL)
## Nicole Aucoin, Harvard (BWH SPL)
## Wendy Plesniak, Harvard (BWH SPL)
## Marianna Jakab, Harvard (BWH SPL)
# Algorithms Core
## [http://www.cs.utah.edu/~whitaker/ Ross Whitaker], Utah PI
## [[Eric_Grimson|Eric Grimson]], MIT
## [[Polina_Golland|Polina Golland]], MIT PI
## [[User:Styner|Martin Styner]], UNC PI
## Ipek Oguz, UNC
## Gary Long, UNC
## Allen Tannenbaum, Georgia Tech, PI
## Oleg Michailovich, Georgia Tech
## Preston T. Fletcher, Utah
## Ran Tao, Utah
## [http://www.cs.unc.edu/~gerig/ Guido Gerig], UTAH
## [[User:Gcasey|Casey Goodlett]], UTAH (Guido)
## [http://www.massgeneral.org/ncs/neuro_faculty_Kennedy.htm/ David Kennedy], Ph.D, Harvard (MGH) PI
## Bruce Fischl, Ph.D, Harvard (MGH)
## [[User:nicks|Nick Schmansky]], Harvard (MGH)
## [[User:dsjen|Dennis Jen]], Harvard (MGH)
## Daniel Marcus, Washington Univerity at St. Louis
# Engineering Core
## [[User:Lorensen|Will Schroeder]], Kitware PI
## [[User:Ibanez|Luis Ibanez]], Kitware
## [http://www.kitware.com/profile/team/hoffman.html/ William Hoffman], Kitware
## [[User:Barre|Sebastien Barre]], Kitware
## [[User:Millerjv|Jim Miller]], GE PI
## [[User:Taox|Xiaodong Tao]], GE
## Dan Blezek, GE
## [[User:Pieper|Steve Pieper]], Isomics PI
## Alex Yarmakovich, Isomics
## Arthur W. Toga, UCLA PI
## Jia Wei Tam, UCLA
## [[User:Jags|Jagadeeswaran Rajendiran]], UCLA
## Mark Ellisman, UCSD PI
## Jeff Grethe, UCSD
## [http://www.cse.ucsd.edu/~ncjones/bio.html/ Neil Jones], UCSD
## Bryan W. Smith, UCSD Supplement
# Driving Biological Problems (DBP)
## [http://www.cisst.org/~gabor/ Gabor Fichtinger], Queen's
## [http://media.cs.queensu.ca/purang/ Purang Abolmaesumi], Queen's
## [http://imaging.robarts.ca/~dgobbi/ David Gobbi], Queen's
## Jonathan Boisvert, Queen's
## Jeremy Bockholt, The Mind Institute
## Chuck Gasparovic, The Mind Institute
## [http://www.med.unc.edu/psych/directories/hazlett.htm/ Heather Cody Hazlett], UNC
## Clement Vachet, UNC
## Gary Long, UNC
## Rachel Gimpel Smith, UNC
## [http://lmi.bwh.harvard.edu/~kubicki/ Marek Kubicki], BWH
## Usman Khan, BWH
## Marc Niethammer, BWH
## Katharina Quintus, BWH
# Service
## [[User:Will|Will Schroeder]], Kitware PI
## Ken Martin, Kitware
## Brad Davis, Kitware
# Training
## [[User:Randy|Randy Gollub]], Harvard (MGH) PI
## Guido Gerig, UNC
## [[User:Whitaker]], Utah
## Martha Shenton, Harvard (BWH PNL) PI
## [[User:SPujol|Sonia Pujol]], Harvard (BWH SPL)
# Dissemination
## [[User:Tkapur|Tina Kapur]], BWH SPL co-PI
## [[User:Pieper|Steve Pieper]], Isomics co-PI
# Management
## [[User:Sanjay|Sanjay Manandhar]], Harvard (BWH SPL)
## [[User:Rachana|Rachana Manandhar]], Harvard (BWH SPL)

 

== NA-MIC Collaborators ==
These NA-MIC collaborators are funded under the "Collaboration with NCBC" PAR.
#Nicole Grosland, UIowa
#Vincent Magnotta, UIowa
#Steve Pieper, Isomics
#James Daunais, Wakeforest
#Robert Kraft, Wakeforest
#Chris Wyatt, VT
#Kilian Pohl, BWH
#Sandy Wells, BWH
#Kevin Cleary, Georgetown
#Enrique Campos-Nanez, Georgetown
#Patrick (Peng) Cheng, Georgetown
#Ziv Yaniv, Georgetown
#Nobuhiko Hata, BWH

== NA-MIC alumni ==
* [http://marchingcubes.org Bill Lorensen]
* [[User:Lzollei|Lilla Zollei]], MIT
* Lauren O'Donnell, MIT
* [http://people.csail.mit.edu/wanmei/ Wanmei Ou], MIT
* [[Mahnaz_Maddah|Mahnaz Maddah]], MIT
* Ramsey Al-Hakim, Georgia Tech
* [[User:Melonakos|John Melonakos]], Georgia Tech
* [[User:Lankton|Shawn Lankton]], Georgia Tech
* [[User:Nain|Delphine Nain]], Georgia Tech
* Xavier Le Faucheur, Georgia Tech
* [[User:Mohan|Vandana Mohan]], Georgia Tech
* Tom Fletcher, Utah
* [http://www.sci.utah.edu/cgi-bin/SCIpersonnel.pl?username=tolga Tolga Tasdizen], Utah
* [http://www.cs.utah.edu/~sbasu/ Saurav Basu], Utah
* Josh Snyder, Harvard (MGH)
* [[User:DavidTuch|David Tuch]], Harvard (MGH)
* [[User:Karthik|Karthik Krishnan]],Kitware
* [http://www.kitware.com/profile/team/cedilnik.html/ Andy Cedilnik], Kitware
* [[User:Mathieu|Mathieu Malaterre]], Kitware
* [http://www.stat.ucla.edu/~dinov/ Ivo Dinov], UCLA
* [[User:MichaelPan|Michael Pan]], UCLA
* Brendan Flaherty, UCSD
* [[User:Adamc|Adam Cohen]] Harvard (BWH PNL)
* [[User:Markd|Mark Dreusicke]] Harvard (BWH PNL)
* Martha Shenton, Harvard (BWH PNL) PI
* [http://lmi.bwh.harvard.edu/~sylvain/ Sylvain Bouix], Harvard (BWH PNL)
* [http://lmi.bwh.harvard.edu/~marc/ Marc Niethammer], Harvard (BWH PNL)
* [http://synapse.hitchcock.org/bios/saykin.shtml Andy Saykin], Dartmouth PI
* Bob Roth, Dartmouth
* [http://synapse.hitchcock.org/bios/flashman.shtml Laura Flashman], Dartmouth
* [http://www.dhmc.org/providers/dhmc_provider_634.html Thomas McAllister], Dartmouth
* Alan Green, Dartmouth
* John West, Dartmouth
* [http://synapse.hitchcock.org/bios/mchugh.shtml/ Tara McHugh], Dartmouth
* [http://synapse.hitchcock.org/bios/pixley.shtml Heather Pixley], Dartmouth
* Stephen Guerin, Dartmouth
* John MacDonald, Dartmouth
* [http://www.bic.uci.edu/faculty/sgpotkin.htm/ Steve Potkin], UCI PI
* [[User:Jfallon|James Fallon]], UCI
* Jessica Turner, UCI
* Martina Panzenboeck, UCI
* David Medina, UCI
* [http://www.ics.uci.edu/~smyth/ Padhraic Smyth], UCI
* [http://www.ics.uci.edu/~sternh/ Hal Stern], UCI
* Diane Highum, UCI
* [http://www.ess.uci.edu/~yu/ Yi Jin], UCI
* Liv Trondsen, UCI
* Fabio Macciardi, Toronto
* [http://www.utpsychiatry.ca/dirsearch.asp?id=130 Jim Kennedy], Toronto
* Aristotle Voineskos, Toronto

== "Friends and Family" ==

=== NIH ===

* [[User:Lysterp|Peter M. Lyster]]

=== mBIRN ===

* [[User:Akolasny|Anthony Kolasny]]
* [[User:Dmarcus|Dan Marcus]]
* [[User:Kikinis|Ron Kikinis]]

=== fBIRN ===

* [[User:Kikinis|Ron Kikinis]]

=== IGT ===

* [[User:Ibanez|Luis Ibanez]]
* [http://wiki.na-mic.org/Wiki/index.php/Slicer-IGT Nobuhiko Hata]

=== Other ===

User:Nicks

2007-11-21T21:38:28Z

Nicks: New page: Nick Schmansky Software Engineer Martinos Center for Biomedical Imaging Massachusetts General Hospital nicks AT nmr DOT mgh DOT harvard DOT edu

Nick Schmansky

Software Engineer

Martinos Center for Biomedical Imaging

Massachusetts General Hospital

nicks AT nmr DOT mgh DOT harvard DOT edu

Algorithm:MGH

2007-04-15T00:08:10Z

Nicks: /* In Development */

== Coordination ==

[[Algorithm:MGH:Coordination:Meeting_2004-11-17|2004-11-17 meeting @ MGH]]

[[Algorithm:MGH:Coordination:Meeting_2005-1-21|2005-1-21 meeting @ BWH]]

== Projects ==

=== In Development ===

'''QDEC: An easy to use GUI for group morphometry studies'''
* Difficulty: Medium
* Impact: High
* '''Use cases''': See [http://surfer.nmr.mgh.harvard.edu/fswiki/QdecProject Qdec project page]

* '''Use case'''<nowiki>: 'Compare the primary eigendirection in two groups to see if they are the same' </nowiki>
* Difficulty: Low
* Impact: Medium

See [http://surfer.nmr.mgh.harvard.edu/fswiki/Qdec Qdec user page]

 ''''Optimal path calculator (Poistats)'''

* '''Use case'''<nowiki>: 'Specify 2 points in a diffusion image and tell how connected they are.' </nowiki>
* Difficulty: High
* Impact: High

See [[AHM_2006:ProjectsDTIPathOfInterest|AHM 2006:ProjectsDTIPathOfInterest]]

 '''Statistical power benefit of ITK nonlinear registration'''

* '''Use case'''<nowiki>: 'Evaluate benefit of using ITK nonlinear registration for group FA comparisons' </nowiki>
* Difficulty: Low-Medium
* Impact: Medium

See: [[Engineering:Project:Non-rigid_EPI_registration|Engineering:Project:Non-rigid_EPI_registration]]

 '''Adding NRRD I/O to Freesurfer'''

* '''Use case'''<nowiki>: 'Open a NRRD volume in FreeSurfer.' </nowiki>
* '''Use case'''<nowiki>: 'Convert an MGH volume to a NRRD volume with Freesurfer.' </nowiki>
* Difficulty: Low
* Impact: Medium

# Write unit tests for new IO functions (Snyder): '''in progress'''
# Add NrrdIO libraries from Teem to FS source tree, build with autoconf (Snyder): '''done'''
# Write and test FS NRRD IO functions (Snyder, Kindlmann): '''in progress'''
# Develop approriate headers for MGH DWI data (Teich): '''queued'''
# Automate header generation when possible (Teich): '''queued'''

 '''Cortical Surface Shape Analysis Based on Spherical Wavelets'''

 '''Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops'''

 

* '''queued''' - step identified/specd
* '''in progress''' - step in progress
* '''done''' - step complete

=== Done ===

'''QBALL visualization'''

* '''Use case'''<nowiki>: 'Visualize q-ball data in Slicer.' </nowiki>
* Difficulty: Low
* Impact: Medium

# Implement ODF polygon decimation algorithm (Tuch) : '''done'''
# Port decimation fileformat into FreeDiffusion Visualizer (Snyder) : '''done'''
# Port QBALL/ODF visualization into [[Slicer|Slicer]] (Estepar/Snyder/Kindlmann/Tuch/Westin): '''done'''
## Implement (Estepar): '''done'''
## Test on mock data set (Estepar): '''done'''
## Demo for real data set (Estepar/Snyder/Kindlmann): '''done'''

'''Tensor-based group comparison (Cramer test)'''

* '''Use case'''<nowiki>: 'Compare DTI images between groups using the full tensor information.' </nowiki>
* Difficulty: Medium
* Impact: Medium-High

# Implement in R (Whitcher/Tuch) : '''done'''
# Power analysis (Whitcher) : '''done'''
# Port to Matlab (Whitcher) : '''done'''
# Validate Matlab version against R (Whitcher) : '''done'''
# Test on group data : '''done'''
# Release bootstrap-only version to test group: '''done'''
# Port FFT method from R to matlab (Whitcher): '''done'''
# Implement FFT method in diffusion development environment (Tuch): '''done'''

See [[Algorithm:MGH:Development:GroupComp|Algorithm:MGH:Development:GroupComp]]

 '''Replacing Numerical Recipes in FreeSurfer (for open sourcing)'''

* '''Use case'''<nowiki>: 'Unit tests pass with all replacements.' </nowiki>
* Difficulty: Medium-High
* Impact: High

# Write test cases for each algorithm (Snyder, Jen): '''done'''
# Identify replacements (Snyder, Jen): '''done'''
# Integrate required libraries into FreeSurfer build process (Snyder, Jen): '''done'''
# Iteratively replace recipes with substitutes and run tests (Snyder, Jen): '''done'''

 '''Intensity Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms'''

* '''Use Case'''<nowiki>: 'Atlas-based fully automated whole brain segmentation' </nowiki>
* Difficulty: Medium-High
* Impact: Medium-High

# Implemented in C and distribute with the FreeSurface Package: '''done'''

Mbirn:SpringConference2007

2007-03-19T18:57:29Z

Nicks: qdec

== Morphometry BIRN All Hands Meeting - 2007 ==
 
Location: Boston, MA
 
Date: March 21-23, 2007
 
Venue: [http://www.radisson.com/bostonma Radisson Boston]
 
 
Room rates are $159/night, please call the hotel directly at 617-482-1800 and let reservations know that you are with the "mBIRN Spring Meeting 2007" block.
 
 
There will be a Programmer's Day on 3/21 for those people needing extra working time outside of the two meeting days. Contact
Karl Helmer (helmer@nmr.mgh.harvard.edu) for more information.
 
 
We will be kicking off the meeting on 3/21 with a cocktail party social starting at 5:00 pm followed by a dinner at 7:00 pm, both are at the Radisson. 

There is also a group dinner planned on 3/22 at Maggiano's Little Italy (4 Columbus Ave, Boston, MA
(617) 542-3456; across the street from the hotel).
 
 

== Sessions for AHM: ==

'''Wednesday (3/21) ''' ''Note: this session takes place at [http://spl.harvard.edu:8000/pages/directions/1249Boylston/Boylston-facility.html 1249 Boylston Street]'' 
The conference room is on the second floor, room 245, and the phone number
there is 617-525-6224. Once you arrive downstairs call extension 56224 and
someone can come and let you in.

*Morning 
Breakfast: 8AM 
Start: 8:30 AM 
Topic 1: Where are we and where are we going? 
Introduction / Goals of the Meeting - Randy Gollub 
Data Repository Update - Chris Fennema-Notestine 
(5 minute presentations + time for discussion/questions) 
Portal Update - Jeff Grethe, Mark James 
Applications Update: Steve Pieper (Slicer 3); Nick Schmansky (QDEC: a Freesurfer group-analysis GUI tool) 
Workflows Update: R. Jagadeeswaran (LONI); Shawn Murphy/Mike Mendis (MGH) 
Databases Update - Dan Marcus (XNAT) 

Discussion of the agenda for Wednesday and Thursday 
Move to Breakout Groups 

''Lunch (12-1:00PM)'' 
*Afternoon (1-4PM) 
Database/Application Integration Working Session (Dan Marcus, Steve Pieper, Doug Greve, Tim Olsen) 
mBIRN workflows group (Anthony Kolasny, Tim OBrien, Shawn Murphy, Mike Mendis, Ramil Manansala, Neil Jones) 
 
*Evening (at Radisson) 
Cocktail Reception (@5:30 PM) 
Site Update Presentations (@6:00PM) 
Dinner (@7:00 PM) 

'''Thursday (3/22)''' 
*Morning 
Breakfast 7:30-8:30 
Introduction – Bruce Rosen 
Chris Fennema-Notestine – update from Data Repository Director 

Specific sessions for the following TBD by discussion on Wednesday's Programmer Day: 
Workflows Working Group-demonstrations of the LDDMM and LONI pipelines 
XCEDE Group (Dan Marcus, Jeff Grethe, Tim O'Brien, Dave Keator,..., includes provenance group) 
Upload Working Group 
Visualization and Analysis Working Group 
 
A parallel DTI Working Session (Susumu Mori, Allen Song, Jonathan Farrell, Bennet Landman, Jim Fallon, Berj Gimi, Anders Dale, Karl Helmer) will run throughout Thursday. 
''DTI session will begin with 5 minute updates from each participating site'' 

''Lunch 12-2PM'' 
Operations Committee Lunch 12-1 PM 

*Afternoon (2-5PM) 
DTI Working Session 
Workflows Working Session 
XCEDE /Query Atlas groups (includes provenance group)
Upload Working Session 
Databases Working Session 

*Evening 
6:30 pm - Dinner at Maggiano's Little Italy (4 Columbus Ave, Boston, MA
(617) 542-3456; across the street from the hotel) 

'''Friday (3/23)''' 
*Morning 
Breakfast (7:30-8:30) 
Opening Remarks - Greg Farber 
Opening Remarks II - Bruce Rosen 
Overview for each Working Group 
Introduction to the BIRN Data Repository - Christine Fennema-Notestine 
Introduction of the Clinical Collaborators - Representatives 
Roundtable discussion with mBIRN clinical collaborators: National Database for Autism Research (NDAR), Specialized Program of Translational Research in Acute Stroke (SPOTRIAS), Framingham Heart Study, NIMH Pediatric Neuroimaging Group.
 

''Lunch (12-1:30PM)'' 

*Afternoon (1:30-4PM) 
Wrap up Session – Discussion of what Morphometry can deliver in the one and
two year timeframes. Incorporation of feedback and needs of the clinical
collaborators. 
Action Items 
Finish @ 4pm
 
 

== ATTENDEES ==
 
MGH: B. Rosen, R. Gollub, K. Helmer, S.Murphy
 
BWH: N. Aucoin, K. Hayes, S. Pieper, R. Kikinis
 
MIT: P.Golland
 
WashU: D. Marcus, T.Olsen, M. Ramaratnam, K. Archie
 
JHU: T. Brown, J.Hennesey, A. Kolasny
 
Duke: A. Song, B. Boyd, J. MacFall, K. Zhao
 
UCLA: A. Toga
 
UCSD: A. Dale, C. Fennema-Notestine
 
UCI: S. Potkin, J.Turner, Dingying Wei
 
BIRN CC: M. James
 
NIH: E.Collier
 
NA-MIC:
 
Other Collaborators: