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

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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...]]
 
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...]]
  
</font> Future research is needed to establish critical values for diffusion sequence acquisition parameters that would allow diffusion data processing via Slicer.
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<font color="red">'''New: '''</font> Future research is needed to establish critical values for diffusion sequence acquisition parameters that would allow diffusion data processing via Slicer.
  
 
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Revision as of 14:37, 15 May 2009

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Diffusion Image Analysis

Tractography Methods

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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, 2007.

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: Wang P, Saykin A, Flashman L, Wishart H, Rabin L, Santulli R, McHugh T, MacDonald J, Mamourian A. Regionally specific atrophy of the corpus callosum in AD, MCI and cognitive complaints. Neurobiol Aging. 2006 Nov;27(11):1613-7.

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Joint Registration and Segmentation of DWI Fiber Tractography

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. More...

New: Ziyan U, Sabuncu M, Grimson W, Westin C. A Robust Algorithm for Fiber-Bundle Atlas Construction. In Mathematical Methods in Biomedical Image Analysis (MMBIA 2007): 2007 IEEE Workshop, ICCV 2007 workshop. Rio de Janeiro, Brazil, 2007.

FiberTracts-angle.jpg

DTI Volumetric White Matter Connectivity

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. More...

New: Fletcher P, Tao R, Jeong W, Whitaker R. A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI. Inf Process Med Imaging. 2007;20:346-358.

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Stochastic Tractography

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. More...

Clustering and Quantitative Analysis

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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.

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DTI Fiber Clustering and Fiber-Based Analysis

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. More...

New: Monica E. Lemmond, Lauren J. O'Donnell, Stephen Whalen, Alexandra J. Golby. Characterizing Diffusion Along White Matter Tracts Affected by Primary Brain Tumors. Accepted to HBM 2007.

Models.jpg

Fiber Tract Modeling, Clustering, and Quantitative Analysis

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. More...

New: M. Maddah, W. E. L. Grimson, S. K. Warfield, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts. Medical Image Analysis, in press.

Maddah M, Wells W, Warfield S, Westin C, Grimson W. Probabilistic clustering and quantitative analysis of white matter fiber tracts. Inf Process Med Imaging. 2007;20:372-83.

NAMIC UncinateFasiculus prelim.jpg

Fractional Anisotropy in the Uncinate Fasciculus

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. More...

New: AHM 2007: Training on fiber tractography in Slicer with Sylvain Bouix that we can apply to this project as well as investigation of other fiber tracts such as the cingulate bundle.

Other Diffusion Image Algorithms

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Integrity of Fronto-Temporal Circuitry

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. More...

New: AHM 2007: John West had training session with Dennis Jen on new version of POI algorithm. Worked together to read new Dartmouth 3T Philips Data. Further work ongoing to integrate POI into Slicer 3.

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DTI-based Segmentation

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. More...

New: Ziyan U, Tuch D, Westin C. Segmentation of Thalamic Nuclei from DTI Using Spectral Clustering. Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006, LNCS 4191, pp. 807-814, 2006.Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

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Influence of Imaging Noise on DTI Statistics

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. More...

New: Goodlett C, Fletcher P, Lin W, Gerig G. Quantification of Measurement Error in DTI: Theoretical Predictions and Validation. Proceedings of the 10th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2007, LNCS 4791, pp. 10–17, 2007.

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Notes on the dicom conversion for DTI data

Report about some observation when converting DTI data from dicom to dwi volumes More...

Validation

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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: Contrasting Tractography Methods Conference, Santa Fe, October 1-2, 2007.


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DTI Validation

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. More...

New: Future research is needed to establish critical values for diffusion sequence acquisition parameters that would allow diffusion data processing via Slicer.