Difference between revisions of "NA-MIC Internal Collaborations:DiffusionImageAnalysis"

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Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage, 2009 Oct 15;48(1):21-8
 
Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage, 2009 Oct 15;48(1):21-8
 
   
 
   
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| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
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| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
 
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== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==
  
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We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]
  
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=== Clustering and Quantitative Analysis ===
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<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008.  Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.
  
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{| cellpadding="10" style="text-align:left;"
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<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.
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| style="width:15%" | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
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== [[Projects:DTIPopulationAnalysis|Group Analysis of DTI Fiber Tracts]] ==
 
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Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics.  This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
 
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<font color="red">'''New: '''</font> Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
 
  
 
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| | [[Image:Models.jpg|200px]]
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| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
 
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== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==
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== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==
  
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The goal of this work is to model the shape of the fiber bundles and use this model discription in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]
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We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]
  
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<font color="red">'''New:'''</font> Maddah M., Zollei L., Grimson W.E.L., Westin C., Wells III W.M. , A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis . Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 4543943: 105–108.
 
  
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<font color="red">'''New: '''</font> V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).
  
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| | [[Image:CingulumAllSubjectsFibers.png|200px]]
 
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== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==
 
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The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]
 
  
 
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| | [[Image:ZoomedResultWithModel.png|200px]]
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== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==
  
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In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]
  
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| | [[Image:NAMIC UncinateFasiculus prelim.jpg|200px]]
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<font color="red">'''New: '''</font> J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.
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== [[Projects:FractionalAnisotrophyInTheUncinateFasciculus|Fractional Anisotropy in the Uncinate Fasciculus]] ==
 
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Our objective is to measure the FA in the uncinate fasciculus in patients with schizophrenia. This project is based on the methods published by Kubicki et al. and extends that work by including a bipolar disorder control group, and determining whether there is an association between FA and cognitive functioning and symptoms in the patient groups. [[Projects:FractionalAnisotrophyInTheUncinateFasciculus|More...]]
 
  
 
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=== Other Diffusion Image Algorithms ===
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=== Clustering and Quantitative Analysis ===
  
 
{| cellpadding="10" style="text-align:left;"
 
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| style="width:15%" | [[Image:DartmouthPathOfInterest.png|200px]]
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| style="width:15%" | [[Image:Cbg-dtiatlas-tracts.png|200px]]
 
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== [[Projects:IntegrityOfFrontoTemporalCircuitry|Integrity of Fronto-Temporal Circuitry]] ==
 
  
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Our objective is to develop methodology that will permit investigators to specify functional MRI regional of interests (fROI) and determine the optimal white matter pathways between the fROIs based on DTI. [[Projects:IntegrityOfFrontoTemporalCircuitry|More...]]
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== [[Projects:DTIPopulationAnalysis|Group Analysis of DTI Fiber Tracts]] ==
  
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Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics.  This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. [[Projects:DTIPopulationAnalysis|More...]]
  
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| | [[Image:Thalamus_algo_outline.png|200px]]
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<font color="red">'''New: '''</font> Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
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== [[Projects:DTISegmentation|DTI-based Segmentation]] ==
 
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Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]
 
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== [[Projects:DTINoiseStatistics|Influence of Imaging Noise on DTI Statistics]] ==
 
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Clinical acquisition of diffusion weighted images with high signal to noise ratio remains a challenge. The goal of this project is to understand the impact of MR noise on quantiative statistics of diffusion properties such as anisotropy measures, trace, etc. [[Projects:DTINoiseStatistics|More...]]
 
  
 
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== [[Projects:DicomToNrrdForDTI|Notes on the dicom conversion for DTI data]] ==
 
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Report about some observation when converting DTI data from dicom to dwi volumes
 
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[[Projects:DicomToNrrdForDTI|More...]]
 
  
 
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== [[Projects:DTIValidation|DTI Validation]] ==
 
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To carry out quantitative and qualitative validation of the DTI tractography tools. These will be applied to a limited set of specific tracts in single data sets and single tractography tools, and on several data sets using at least two tractography programs and by investigators in different laboratories. [[Projects:DTIValidation|More...]]
 
  
  
 
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Latest revision as of 18:38, 4 November 2009

Home < NA-MIC Internal Collaborations:DiffusionImageAnalysis
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Diffusion Image Analysis

Tractography Methods

NAMIC callosum tracts prelim.jpg

Corpus Callosum Fiber Tractography

The goal of this project is to examine the integrity of fibers in the corpus callosum in patients with schizophrenia and determine whether this is associated with brain activation during memory tasks. More...

New: Salat D.H., Lee S.Y., van der Kouwe A.J., Greve D.N., Fischl B., Rosas H.D. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage, 2009 Oct 15;48(1):21-8

GTTubSurfaceSeg-Img1.png

Tubular Surface Segmentation Framework

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. More...

New: V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

New: V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.


GT-PopStudyVis OnCBs Case19-View2.jpg

Group Study on DW-MRI using the Tubular Surface Model

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. More...


New: V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).


ZoomedResultWithModel.png

Geodesic Tractography Segmentation

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). More...

New: J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

Clustering and Quantitative Analysis

Cbg-dtiatlas-tracts.png


Group Analysis of DTI Fiber Tracts

Analysis of populations of diffusion images typically requires time-consuming manual segmentation of structures of interest to obtain correspondance for statistics. This project uses non-rigid registration of DTI images to produce a common coordinate system for hypothesis testing of diffusion properties. More...

New: Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.

Validation

Cingulum1.jpg


Contrasting Tractography Measures

This project represents a new initiative to build upon a shared vision among Cores 1, 3 and 5 that the field of medical image analysis would be well served by work in the area of validation, calibration and assessment of reliability in DW-MRI image analysis. More...

New: S. Pujol, C-F. Westin, R. Whitaker, G. Gerig, T. Fletcher, V. Magnotta, S. Bouix, R. Kikinis, W. M. Wells III, and R. Gollub. Preliminary Results on the use of STAPLE for evaluating DT-MRI tractography in the absence of ground truth. In Proceedings of the 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine ISMRM 2009, April 18-24, 2009. Honolulu, Hawaii. ISMRM 2009


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