Tissue Dependent Registration - Revision history

Pettri: /* Key Investigators */

2010-02-08T15:11:33Z

Key Investigators

Pettri: /* Key Investigators */

2010-02-08T15:07:15Z

Key Investigators

Pettri: /* Key Investigators */

2010-01-04T20:23:09Z

Key Investigators

Pettri: /* Key Investigators */

2010-01-04T20:16:50Z

Key Investigators

Pettri: /* Key Investigators */

2010-01-04T20:11:32Z

Key Investigators

Pettri: /* Key Investigators */

2010-01-04T20:07:21Z

Key Investigators

Pettri at 14:22, 4 December 2009

2009-12-04T14:22:51Z

Pettri at 14:21, 4 December 2009

2009-12-04T14:21:40Z

Pettri at 14:20, 4 December 2009

2009-12-04T14:20:05Z

Pettri at 14:17, 4 December 2009

2009-12-04T14:17:32Z

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 The NAMIC kit is currently being utilized for visualizing results from the registration algorithm. Each MCMC sample provides a new configuration of the FE-mesh. We visualize this in realtime with each element colored according to the elastic parameter values. More interestingly, characterization of the posterior allows for quantification and visualization of
 the uncertainty of the deformation field. We have developed different tools for visualizing and interacting with these uncertainty maps.
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 <h3>Progress</h3>
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← Older revision		Revision as of 20:23, 4 January 2010
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	<h3>Approach, Plan</h3>		<h3>Approach, Plan</h3>
		+	Automatic segmentation into Skull, CSF, WM, GM of clinical dataset.
		+
	Generate tissue dependent tetrahedral meshes.		Generate tissue dependent tetrahedral meshes.

← Older revision		Revision as of 20:16, 4 January 2010
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	Optimize FE-computations.		Optimize FE-computations.
		+
		+	Develop tool for visualizing a tetrahedral mesh with varying elastic parameters.
	</div>		</div>

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 Most registration methods treat tissue parameters as uniform parameters over the image domain. For instance, when brain tissue is modeled as an elastic material, identical stiffness parameters are used for all tissue types (CSF, GM, WM). In our approach, we let each tissue type take on varying stiffness parameters and solve for the deformation as well as the stiffness parameters at the same time.
-We pose the elastic registration problem as a Bayesian problem where we marginalize out the elastic parameters. A Markov Chain Monte Carlo (MCMC) sampling technique is applied to characterize the posterior. However, even though the elastic parameters are marginalized out, we can retain the elastic samples and also characterize the distribution of the elastic parameters.
+We pose the elastic registration problem as a Bayesian problem where we marginalize out the elastic parameters. A Markov Chain Monte Carlo (MCMC) sampling technique is applied to characterize the posterior.
 We use a linear elastic Finite Element (FE) model to compute the likelihood (similarity term) and the deformation prior (regularizer). In a FE registration model, tissue is modeled by a mesh. To accurately model the tissue and let elastic parameters vary according to tissue type, we require a tissue dependent tetrahedral mesh. Assuming we have a segmentation of the brain tissue into CSF, GM and WM, we need a mesh where the tetrahedra conform to the different tissue types (the edges of a tetrahedra do not cross from one tissue type to the other but are either fully within a tissue type or on the boundary between two tissue types).

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 <h3>Objective</h3>
-Most registration methods treat tissue parameters as uniform over the image domain. For instance, when brain tissue is modeled as an elastic material, identical stiffness parameters are used for all tissue types (CSF, GM, WM). We let different tissue types take on varying stiffness parameters, so we can thereby optimize the deformation as well as the stiffness parameters at the same time. Our approach models the tissue with a linear elastic Finite Element (FE) model and solve for the linear elastic tissue parameters as well as the deformation through a Markov Chain Monte Carlo process.
+Most registration methods treat tissue parameters as uniform parameters over the image domain. For instance, when brain tissue is modeled as an elastic material, identical stiffness parameters are used for all tissue types (CSF, GM, WM). In our approach, we let each tissue type take on varying stiffness parameters and solve for the deformation as well as the stiffness parameters at the same time.
-However, to accurately model the tissue, we require a tissue dependent tetrahedral mesh. Assuming we have a segmentation of the brain tissue into CSF, GM and WM, we need a mesh where the tetrahedra conform to the different tissue types (the edges of a tetrahedra do not cross from one tissue type to the other but are either fully within a tissue type or on the boundary between two tissue types).
+We pose the elastic registration problem as a Bayesian problem where we marginalize out the elastic parameters. A Markov Chain Monte Carlo (MCMC) sampling technique is applied to characterize the posterior. However, even though the elastic parameters are marginalized out, we can retain the elastic samples and also characterize the distribution of the elastic parameters.
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 <h3>Approach, Plan</h3>
-Find/Develop mesher that generates elements conforming to a labelmap.
+Generate tissue dependent tetrahedral meshes.
-Utilize command line tools to visualize the registration results.
+Optimize FE-computations.
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 Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
 Image:FEMMesh2.jpeg|Slice of a label dependent tetrahedral mesh.
 </gallery>

← Older revision		Revision as of 14:17, 4 December 2009
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		+	__NOTOC__
		+	<gallery>
		+	Image:PW-SLC2010.png\|[[2010_Winter_Project_Week#Projects\|Projects List]]
		+	</gallery>
		+
	==Key Investigators==		==Key Investigators==
	* Petter Risholm (PhD Student, Harvard Medical School (BWH), University of Oslo)		* Petter Risholm (PhD Student, Harvard Medical School (BWH), University of Oslo)