View source for Projects:CorticalCorrespondenceWithParticleSystem

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=  Cortical Correspondence with Particle Systems =

[[Image:Sulcaldepth.png|thumb|300px|]]

In this project, we want to compute cortical correspondence on populations,
using various features such as cortical structure, DTI connectivity, vascular
structure. This presents a challenge because of
the highly convoluted surface of the cortex, as well as because of the different
properties of the data features we want to incorporate together.

= Description =

We would like to use a particle-based entropy minimizing system for the correspondence
computation, in a population-based manner. This method best suits
our needs since, parameterization-based methods, such as MDL or SPHARM,
require a spherical parametrization of the surface, which is hard to obtain for
the highly convoluted cortex surface. Another advantage of using the particle-based correspondence technique is that it does not require the surface to be of
spherical topology; this means a lot less pre-processing for our method, since
the brain cortex is not of spherical topology. Another strength of this method is
that it would (eventually) enable correspondence computation on the subcortical structures
and on the cortical surface using the same framework. We also would like to
explore correspondence on the cerebellum, which traditionally is excluded from
such studies (e.g. in FreeSurfer-based work).

The main disadvantage of using the particle-based correspondence technique
on the brain cortex is that it assumes the particles to be existing on local tangent
planes, which presents a challenge for the cortex given the highly convoluted
surface. We propose to overcome this difficulty by first ‘inflating’ the cortex
surface. This way, we obtain a less convoluted, sphere-like surface, where the
particles will be interacting. However, we need a 1-1 correspondence between
the original cortex surface and the inflated surface, since the data to be used
for correspondence, such as the the curvature, and the vascular data, lives on
the original cortex surface. FreeSurfer offers a method that minimizes the distance distortion in the
mapping, while also smoothing the surface. FreeSurfer also preprocesses the
input surface to generate a spherical topology. 

[[Image:CaudateProbCorticalConnectivity.jpg|thumb|300px|]]

Once the framework for computing the correspondence given certain data features
is established, the major challenge is to incorporate the various data forms
that we would like to use together. We are using structural data as well as
connectivity (DTI). We have first tested our method using structural metrics, namely, sulcal depth (as computed by FreeSurfer), and have demonstrated improved correspondence quality compared to traditional, location-only correspondence (using the particle-based entropy framework), and we have shown that our results are at least comparable to FreeSurfer. This comes as no surprise as we had already shown in [[Projects:PopulationBasedCorrespondence|our previous studies]] that correspondence can be enhanced by using local curvature in addition of point locations for objects with complex geometry. Next, we have developed a technique for representing the white matter connectivity information on the cortical surface in a manner that can be incorporated into the correspondence framework. We use probabilistic connectivity maps obtained by performing stochastic tractography (which is a [[Projects:DTIStochasticTractography | separate NA-MIC project]]) from various ROI's. In the image to the right, the cortical connectivity for the left and right caudate is visualized in 2 subjects.We use a cortex deflating scheme to overcome problems related to noisy DTI data near the cortical boundary. We have found that using the connectivity maps in addition to sulcal depth and spatial location further improves correspondence quality.

= Publications =
* Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009, in print.
*[http://www.na-mic.org/pages/Special:PubDB_View?dspaceid=1444  	Oguz I, Cates J, Fletcher T, Whitaker R, Cool D, Aylward S, Styner M. Cortical correspondence using entropy-based particle systems and local features. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2008; 1637-1640.]
* [http://www.na-mic.org/pages/Special:Publications?text=Projects%3ACorticalCorrespondenceWithParticleSystem&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| Cates J, Fletcher P, Styner M, Shenton M, Whitaker R. Shape modeling and analysis with entropy-based particle systems. Inf Process Med Imaging. 2007;20:333-45.]

= Key Investigators =
* UNC Algorithms: Ipek Oguz, Martin Styner
* Utah Algorithms: Josh Cates, Tom Fletcher, Ross Whitaker

[[Category:fMRI]] [[Category:Shape Analysis]] [[Category: Diffusion MRI]]